BLUEMOUNTAIN INVESTMENT RESEARCH WHO IS ON THE … · BLUEMOUNTAIN INVESTMENT RESEARCH 2Executive Summary If you buy or sell a security and expect an excess return, you should have

BLUEMOUNTAIN INVESTMENT RESEARCH

WHO IS ON THE OTHER S IDE?

Being successful as an active investment

manager requires seeking, finding, and

capitalizing on inefficiencies in the market.

Markets cannot be perfectly efficient

because there are costs to gathering

information and reflecting it in prices.

Some forces push prices toward efficiency.

These include a large population of smart and

motivated investors, the increasingly uniform

dissemination of data, and the plummeting

costs of computing and trading.

However, other forces keep markets from the

Platonic ideal of perfect efficiency. Some

sources of valuable information remain

expensive and the cost to capture an

opportunity can be material.

But perhaps the biggest source of market

inefficiency remains the human being. As

groups, we repeatedly veer to extremes in our

optimism or pessimism, leaving mispriced

securities in our wake. And then there are

institutions that play by rules imposed by

regulation, contract, or internal policy. The

result can be actions that make sense for the

institution but create inefficiency in the

market. In short, many inefficiencies remain.

In this report, Michael describes a taxonomy

of inefficiencies, supported by a rich vein of

academic research. The goal is to have a

clear idea of why efficiency is constrained

and why we believe we have an opportunity

to generate an attractive return after an

adjustment for risk. As always, we would be

pleased to discuss specific examples of how

we apply these principles.

Andrew Feldstein

Chief Investment Officer

February 12, 2019

BLUEMOUNTAIN INVEST MENT RE SEAR CH / F EB RUARY 1 2 , 2019

Who Is On the Other Side?

You Need Good BAIT to Land a Winner

B L U E M O U N T A I N I N V E S T M E N T R E S E A R C H

Michael J. Mauboussin

Director of Research

[email protected]

Table of Contents

Executive Summary ................................................................................................................................................2

Introduction .............................................................................................................................................................3

Efficiency Defined ...........................................................................................................................................3

The Market for Information and the Market for Assets .............................................................................3

Noise Traders ....................................................................................................................................................4

Behavioral Inefficiencies ........................................................................................................................................6

Beware of Behavioral Finance ......................................................................................................................7

Overextrapolation ...........................................................................................................................................8

Sentiment ..........................................................................................................................................................9

The Wisdom (and Madness) of Crowds ......................................................................................................9

How Beliefs Spread..........................................................................................................................................11

Analytical Inefficiencies .........................................................................................................................................13

Analytical Skill ...................................................................................................................................................13

Information Weighting ....................................................................................................................................13

Be a Good Bayesian .......................................................................................................................................14

Time Arbitrage ..................................................................................................................................................14

The Power of Stories ........................................................................................................................................17

Informational Inefficiencies ...................................................................................................................................19

Find Out First .....................................................................................................................................................19

Pay Attention ...................................................................................................................................................20

Task Complexity ...............................................................................................................................................20

Technical Inefficiencies .........................................................................................................................................22

Forced Buyers or Sellers ..................................................................................................................................22

The Importance of Fund Flows ......................................................................................................................23

When Arbitrageurs Fail to Show Up ..............................................................................................................23

Summary ...................................................................................................................................................................26

Checklist for Identifying Market Inefficiencies ...................................................................................................28

Appendix A: Agency Theory in Asset Management ........................................................................................29

Appendix B: Factors—Risk or Behavioral? ..........................................................................................................30

Endnotes ...................................................................................................................................................................31

Resources .................................................................................................................................................................39

Books ..................................................................................................................................................................39

Articles and Papers .........................................................................................................................................41

B L U E M O U N T A I N I N V E S T M E N T R E S E A R C H 2

Executive Summary

If you buy or sell a security and expect an excess return, you should have a good answer to the

question “Who is on the other side?” In effect, you are specifying the source of your advantage,

or edge. We categorize inefficiencies in four areas: behavioral, analytical, informational, and

technical (BAIT).

Market efficiency is a topic of great importance for companies and investors. Capital markets

that function well are an essential contributor to the effective allocation of corporate resources.

There are two related but distinct markets to consider: the market for information about assets

and the market for assets. There is a range of costs to acquire information and trade on it. There

should be a return to gathering information in the form of excess returns. Markets are “efficiently

inefficient.”

Behavioral inefficiencies may be at once the most persistent source of opportunity and the most

difficult to capture. Many behavioral inefficiencies emanate from the psychology of belief

formation and the psychology of decision making. It is essential to remember that these

inefficiencies are generally the result of collective, not individual, actions.

Analytical inefficiencies can provide a source of edge versus other investors through having more

analytical skill, weighing information differently, updating views more effectively, operating on a

different time scale, or anticipating a change in the market’s narrative.

Informational inefficiencies offer edge for investors who can legally acquire relevant information

that others don’t have. There is evidence that attention is costly and that some inefficiencies arise

from limited attention. Research shows that complexity slows the process of information diffusion,

so anticipating the impact of information can confer edge.

Technical inefficiencies can generate excess returns for investors on the other side of forced

sellers or buyers, on the correct side of securities perturbed by investor fund flows, and for investors

who can act as liquidity providers when traditional arbitrageurs have limited access to capital

and hence fail to fulfill their normal function.

Institutions do the majority of the buying and selling but are generally ignored in the theory of

asset pricing. Institutions matter and should be the subject of careful consideration.

We include a checklist and a full list of references for further investigation.


Introduction

Market efficiency is a topic of great importance

for companies and investors. Capital markets that

function well are an essential contributor to the

effective allocation of corporate resources.1

Investors seek inefficiencies to generate excess

returns, or returns that are higher than expected

after adjusting for risk. Understanding efficiency

requires us to examine lots of elements, including

the market for information, the interplay between

capital market participants, and inherent frictions.

Our goal is to create a taxonomy of the sources

of inefficiency to provide active investors with a

robust way to think about delivering excess

returns.

Efficiency Defined. The term “efficiency” comes

from physics and measures the relationship

between the input of energy and the output of

useful work. For instance, your body can roughly

translate every 100 calories you eat into about

20-25 calories of useful work. It turns out that level

of efficiency is similar to a common combustion

engine. Neither your body nor a machine can

translate 100 percent of its energy into work

because of friction.2

Markets are not machines, but the idea of

efficiency still applies. In the case of markets, the

input is information and the output is an asset

price that reflects fair value. Eugene Fama, a

professor of finance at the University of Chicago

Booth School of Business and a recipient of the

2013 Nobel Memorial Prize in Economic Sciences

for his work on market efficiency, sums it up this

way: “A market in which prices always ‘fully

reflect’ available information is called

‘efficient.’”3 Just as a perfectly efficient machine

does not exist, neither does a perfectly efficient

market.

Before turning to market inefficiency, we explore

some important ideas that sometimes get short

shrift from academics and practitioners.4 To start,

there are two related but distinct markets to

consider. One is the market for information about

assets and the other is the market for assets.

The Market for Information and the Market for

Assets. In 1980, a pair of finance professors,

Sanford Grossman and Joseph Stiglitz, wrote a

paper called “On the Impossibility of

Informationally Efficient Markets.”5 They argue

that markets cannot be perfectly efficient

because there is a cost to gathering information

and reflecting it in asset prices and therefore

there must be a proportionate benefit in the form

of excess returns. Because collecting information

is costly, active investors need exploitable

mispricings to provide a sufficient incentive to

participate. Lasse Pedersen, a professor of

finance, says that markets must be “efficiently

inefficient.”6 In this market, investors seek to “buy”

information and “sell” profit.

The market for assets concerns the price at which

investors buy and sell fractional stakes in various

assets. Some investors trade based on

information, others trade on data or drivers not

relevant to value, and still others free ride. For

instance, investors in portfolios that mirror indexes

or follow specific rules rely on active managers for

proper price discovery and liquidity.

The market’s ability to translate information into

price is limited by costs. These are commonly

called “arbitrage costs” and include costs

associated with identifying and verifying

mispricing, implementing and executing trades,

and financing and funding securities.7 These costs

create frictions that are commonly understated in

academic research. That said, many of these

costs have come down over time, which has

contributed to greater efficiency in many

markets. For example, Regulation Fair Disclosure,

implemented in 2000, seeks to quash selective

corporate disclosure. In addition, trading costs

have dropped precipitously in recent decades as

the result of deregulation and advances in

technology.

Exhibit 1 summarizes the relationship between the

markets for information and asset prices. Having

a view that is different than what is priced in, as

well as the ability to profit from that view, are

both essential to generating excess returns.


Exhibit 1: The Markets for Information and Assets

Cost to Implement Asset Market

Low High

Cost to Acquire

Information Market

Low Information obvious

Implementation easy

(e.g., short-term Treasuries)

Information obvious

Implementation hard

(e.g., 3Com/Palm)

High Information nonobvious

Implementation easy

(e.g., supply chain impact)

Information nonobvious

Implementation hard

(e.g., VC - biotech)

Source: BlueMountain Capital Management.

Noise Traders. Robert Shiller, a professor of

economics at Yale University who shared the

Nobel Prize with Fama in 2013, created a model

based on the concept of a noise trader.8 These

investors trade “on noise as if it were information”

and “from an objective point of view they would

be better off not trading.”9 The model suggests

that fundamental value and the cost of arbitrage

jointly determine an asset price.

When arbitrage costs are low, markets tend to be

efficient in the classic sense. When arbitrage costs

are high, price and value can meaningfully differ

from one another. Value is defined as the present

value of cash flow. An influential paper on the

topic defined “an efficient market as one in

which price is within a factor of 2 of value, i.e.,

the price is more than half of value and less than

twice value.”10

The main point is that it is reasonable to think

about efficiency, a state where price equals

value, as falling along a continuum from very

inefficient to very efficient. Indeed, in an update

to his classic paper on the topic, Fama proposes

that a more “sensible” version of an efficient

market is one in which prices incorporate

information to the point “where the marginal

benefits of acting on information (the profits to be

made) do not exceed the marginal costs.”11

This suggests a useful distinction between “prices

are right” and “no free lunch.”12 Prices are right

means that price is an unbiased estimate of

value. No free lunch says that there is no

investment strategy that reliably generates excess

returns. A common argument for market

efficiency is that very few investment managers

consistently deliver excess returns. If prices are

right, it stands to reason that there is no free

lunch.

But the opposite is not true. There can be no free

lunch even when prices are wrong if the cost and

risk of correcting a mispricing are sufficiently high.

Identifying and exploiting these pockets of

inefficiency should be the main focus of active

managers.

The joint hypothesis problem also hampers the

ability to come up with a definitive answer to the

question of market efficiency.13 The first

hypothesis is that an asset-pricing model predicts

asset price returns. Academics and practitioners

commonly use the capital asset pricing model

(CAPM), which describes the relationship

between systematic risk and expected returns.

The second hypothesis is that the market is

efficient.

The basic problem is that you can consider an

asset return anomalous only if you have an

accurate asset-pricing model. As a

consequence, what you deem to be an

anomalous return may be the result of an

inaccurate asset-pricing model, a market

inefficiency, or both. Bear this in mind any time

you hear or see a discussion of market anomalies.

Every time that you buy or sell a security and

anticipate excess returns you should ask, “Who is

on the other side?” Ideally, you should

understand your counterparty’s motivation and

ask why you have an edge. We also know that

institutions, generally absent in asset pricing

models, are very important in practice (see

Appendix A: Agency Theory in Asset

Management). Ed Thorp, a mathematician and


legendary hedge fund manager, suggests that

you have edge when you “can generate excess

risk-adjusted returns that can be logically

explained in a way that is difficult to rebut.”14 In

other words, you have a good answer to the

question “Who is on the other side?”

The challenge is that sophisticated investors

exploit the anomalies that academic research

finds.15 There are actually a couple of things

going on. First, a large percentage of factors that

are correlated with excess returns are the result of

statistical bias. When there are a lot of data and

a lot of relationships, some factors will correlate

with good past returns but will have no predictive

value. This has led some researchers to call for a

higher hurdle than what academic journals

commonly demand to claim that a factor is

effective.16

Second, some factors that predict excess returns

get bid up by smart investors and the opportunity

is competed away. This is especially true when

the cost to do so is not prohibitive. This is the

nature of markets. Exploitable opportunities do

not last long if investors can identify and capture

them at a reasonable cost.

We now turn to a taxonomy of structural

inefficiencies based on behavioral, analytical,

informational, or technical (BAIT) sources. Most of

the inefficiencies we will describe are the result of

multiple sources, but we will attempt to place

opportunities in the category that makes the

most sense. To be an active investor, you must

believe in inefficiency and efficiency. You need

inefficiency to get opportunities, and efficiency

for those opportunities to turn into returns.


Behavioral Ineff iciencies

A behavioral inefficiency exists when an investor,

or more likely a group of investors, behave in a

way that causes price and value to diverge.

Behavioral inefficiencies may be at once the

most persistent source of opportunity and the

most difficult to capture. The persistence stems

from human nature, which does not change

rapidly. Ben Graham, the father of value

investing, said it this way:17

Though business conditions may change,

corporations and securities may change and

financial institutions and regulations may

change, human nature remains essentially

the same. Thus the important and difficult

part of sound investment, which hinges upon

the investor’s own temperament and

attitude, is not much affected by the passing

years.

The difficulty stems from the fact that humans are

social beings and investing is inherently a social

activity. Graham used the parable of Mr. Market

to make the point: You own a small stake in a

private company that costs you $1,000. One of

your partners is an obliging fellow named Mr.

Market who tells you, every day, what he thinks

your stake is worth and, further, offers a price at

which he’s willing to buy you out or offer you an

additional interest. Mr. Market represents the

collective action of investors.

In his telling of the story, Warren Buffett, chairman

and chief executive officer (CEO) of Berkshire

Hathaway and Graham’s most successful

student, goes on to say that Mr. Market has

“incurable emotional problems.” He writes,

“Sometimes he is euphoric and sees only

favorable outcomes and hence names a very

high buy-sell price. Other times he is depressed

and sees only negative outcomes and provides a

very low buy-sell price. Mr. Market is there to serve

you, not to guide you. It is his pocketbook, not his

wisdom, that you will find useful.”18 The point is

that while markets generally offer sensible prices,

there have been and will continue to be bouts of

extreme optimism and pessimism.

This leads to why behavioral inefficiencies are so

hard to exploit. The very driver of behavioral

inefficiency, correlated beliefs, makes it difficult

to take advantage of the opportunity. Most of us

have a powerful desire to be part of the crowd

and an aversion to being separate from the

crowd. The psychological pull to conform is

strongest at the extremes of fear and greed.19

There are at least a couple of good reasons to

consider the behavioral influence on asset prices.

First, only a fraction of asset price moves can be

directly linked to changes in fundamentals, such

as revisions in cash flow or interest rate

expectations. This has been established by studies

of the biggest moves in the stock market since

the 1940s that looked to the media for a

fundamental explanation after the fact. In many

cases, there is no clear fundamental driver of

value. Exhibit 2 shows a summary of the top 10

moves in the Center for Research in Security

Prices (CRSP) value-weighted index of stocks and

the media’s explanation for the change.

A recent study concluded, “Only a minority of the

50 largest moves in the last 25 years can be tied

to fundamental economic information that could

have had a pronounced impact on cash-flow

forecasts or discount rates.”20 These studies make

it clear that asset price changes have

fundamental and behavioral sources.


Exhibit 2: Largest Moves in U.S. Equities, 1988-2018

Date Return News

1

October 13, 2008 11.5%

Governments throughout the world announce moves to support troubled banks.

2

October 28, 2008 9.5% Late rally on Wall Street as rebound in stocks defies latest economic news.

3

October 15, 2008 -9.0%

Falling retail sales and rising wholesale prices spikes fears of recession and erases

Monday's record rally. 4

December 1, 2008 -8.9%

Obama reveals national security team. NBER says U.S. entered recession in

December 2007. Bernanke warns of weak economic conditions. 5

September 29, 2008 -8.3%

$700 billion TARP bill rejected by House of Representatives. President Bush

disappointed. 6

October 9, 2008 -7.3%

Rising fears of global recession pushed Wall Street into freefall. U.S. Treasury may

take stakes in major banks. 7

November 20, 2008 -7.0%

Another wave of selling roiled Wall Street. Democrats say no to current plan for

auto bailout telling industry to come back next month with a detailed plan. 8

March 23, 2009 6.9%

Secretary Geithner makes second attempt at unveiling Obama Administration's

plan to deal with the banking crisis. Obama wants to expand clean energy effort. 9

August 8, 2011 -6.9%

Wall Street had its worst day since the 2008 financial crisis, as fearful investors

reacted to the United States losing its coveted AAA credit rating. 10

November 13, 2008 6.8%

Dow down 300 points on the morning reverses on new investor confidence and

ends up 553.

Source: Bradford Cornell, “What Moves Stock Prices: Another Look,” Journal of Portfolio Management, Vol. 39, No. 3,

Spring 2013, 32-038.

Note: Returns reflect CRSP value-weighted index of firms in the New York, American, and NASDAQ stock exchanges.

Another reason to consider behavioral influence

is that we observe certain patterns in nearly all

markets.21 For example, we have seen bubbles

and crashes in a multitude of geographies (e.g.,

Americas, Europe, and Asia) and asset classes

(e.g., stocks, real estate, and cryptocurrencies).

There is even evidence of similar behaviors in

other primates. For instance, capuchin monkeys

exhibit loss aversion, the tendency to suffer more

from losses than to enjoy gains of a similar size.22

Beware of Behavioral Finance. Behavioral

economics shows how psychological factors can

lead individuals or organizations to make

decisions that deviate from economic theory. It

also shows how the use of heuristics, or rules of

thumb, can lead to biases that affect choices.

This body of research is extremely valuable to

anyone who seeks to make thoughtful and

unbiased decisions.

But it is important to recognize that individual

errors, however widespread, are rarely relevant in

determining market efficiency. The interaction of

investors with little information or rationality can

yield prices with surprising efficiency. The essential

conditions include the presence of investors with

sufficiently heterogeneous views and decision

rules and having an effective way to aggregate

the information. The researchers who wrote one

of the seminal papers on the topic summarize

their finding as follows:23

Allocative efficiency of a double auction

market derives largely from its structure,

independent of traders’ motivation,

intelligence, or learning. Adam Smith’s

invisible hand may be more powerful than

some may have thought; it can generate

aggregate rationality not only from individual

rationality but also from individual irrationality.

The lesson is that you cannot extrapolate from

individuals, who fail to operate according to the

rules of rationality, to markets. The reason is that

individual errors can cancel out, leading to

accurate prices. You can be an overconfident

buyer and I can be an overconfident seller and

the net result is a correct price. The key is

understanding when the wisdom of crowds flips

to the madness of crowds. And the essential

insight is that it has to do with a violation of one or

more of the core conditions for a wise crowd.

Critical to understanding behavioral sources of

inefficiency is identifying when the beliefs of


investors correlate with one another and push

price away from value. Some strong believers in

efficient markets claim that behavioral

explanations are a compilation of stories that

researchers craft to fit the facts.24 Nicholas

Barberis, a professor of finance at the Yale School

of Management, suggests that many of the key

concepts in behavioral finance are based on the

psychology of belief formation and the

psychology of decision making.25

Overextrapolation. Overextrapolation, the

excessive projection of recent experience, is one

of the key ideas behind the psychology of belief

formation. For example, financial economists

have shown that investor expectations for future

stock returns in the next year are highly

correlated with returns in the past year. Exhibit 3

shows the percentage of household equity and

fixed income investments that are allocated to

equities and subsequent five-year stock market

returns. Investors expect high returns after

realizing high returns and expect low returns after

realizing low returns.26

Because stock prices are more volatile than

corporate earnings, valuations tend to be higher

following a period of strong price advances and

lower subsequent to price declines. In contrast

with expectations as the result of

overextrapolation, high valuations are associated

with low expected returns, and low valuations

with high expected returns. This relationship holds

for asset classes beyond stocks, including bonds,

real estate, and sovereign debt.27

Avoiding this type of overextrapolation demands

the ability to “disregard mob fears or enthusiasms

and to focus on a few simple fundamentals.”28

Seth Klarman, founder, chief executive officer,

and portfolio manager of The Baupost Group,

captured the concept beautifully when he said,

“Value investing is at its core the marriage of a

contrarian streak and a calculator.”29 The

“contrarian” part demands an examination of

the other side of the popular view. The

“calculator” part ensures that valuation is

sufficiently extreme to generate excess returns.

Academics have developed a model of a

financial bubble, a sharp rise in an asset price

over a short period of time leading to a lofty

valuation, based on extrapolation. The model

reflects the fact that almost all bubbles are

preceded by good fundamental news and have

abnormally high trading volume driven by

“wavering extrapolators.”30 The main challenge

to investing following sharp price increases is they

are not reliably associated with unusually low

prospective returns. However, these run-ups are

associated with a greater probability of a crash.31

Exhibit 3: Household Equity Share and Future Five-Year Stock Returns, 1953-2018

Source: Bloomberg and Board of Governors of the Federal Reserve System, Division of Research and Statistics,

Balance Sheet of Households and Nonprofit Organizations and Households’ Financial Assets and Liabilities.

Note: Stock returns are total shareholder returns for the S&P 500; Through the third quarter of 2018.

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

1953

1958

1963

1968

1973

1978

1983

1988

1993

1998

2003

2008

2013

2018

Futu

re 5

-Ye

ar

Sto

ck R

etu

rns,

An

nu

aliz

ed

(Axis

In

ve

rte

d)

Ho

use

ho

ld E

qu

ity S

ha

re (

Pe

rce

nt)

Household Equity Share

Future 5-Year Stock Returns

High expectations

Low future returns

Low expectations

High future returns


Overextrapolation is also associated with the

momentum effect, the observation that the

direction of a stock’s return in the next six months

tends to follow the direction of the stock’s return

in the prior six months. For example, a stock that

has done well in the last half year will do well in

the upcoming six months before reversing. More

strictly, a strategy to take advantage of the

momentum effect is more rigorous than the

simple extrapolation by return chasers.32

Performance chasing is another manifestation of

overextrapolation.33 Both retail and institutional

investors have a tendency to buy funds that have

done well and sell those that have performed

poorly. For example, a study of pension plan

sponsors found that in the two years preceding a

decision to fire or hire, the investors they fired had

underperformed, and the investors they hired

had outperformed, their benchmarks.

The decision to fire or hire is evidence of

overextrapolation. Exhibit 4 shows the result of

one study of more than 3,400 plan sponsors. The

data reveal that the fired managers generate

higher returns than the hired managers in the

following two years.34 Economists doing related

work conclude, “Clearly, plan sponsors could

have saved hundreds of billions of dollars in assets

if they had simply stayed the course.”35

Overconfidence is another notable aspect of the

psychology of belief formation. It has a few forms:

Overestimation means you think you are

better than you are (you think you type 60

words per minute but actually type only 40).

Overplacement means you think you are

better than others (93 percent of American

drivers rate their skill as above the median).

Overprecision means you believe you know

the truth with greater accuracy than you

actually do (ask portfolio managers for an

estimate with a 90 percent confidence

interval and they are correct only about 50

percent of the time).36

Overconfidence is associated with lots of trading

activity, which is mostly deleterious to investment

returns.37 Overconfidence tends to build when

asset prices are rising. For example, growth stocks,

defined as the top quintile of stocks based on

price-to-book ratios, generally have substantially

higher turnover than value stocks, the bottom

quintile of stocks based on price-to-book ratios.38

Exhibit 4: Plan Sponsors Buy High and Sell Low

Source: Amit Goyal and Sunil Wahal, “The Selection and

Termination of Investment Management Firms by Plan

Sponsors,” Journal of Finance, Vol. 63, No. 4, August

2008, 1805-1847.

Note: Performance reflects 2-year cumulative excess

returns relative to an appropriate benchmark.

Sentiment. Finance academics have created

sentiment indexes to capture when investors

appear too optimistic or pessimistic.39 Measures

that explain sentiment include trading volume,

indicators of valuation, and the volume and

returns to initial public offerings. Sentiment most

affects the stocks of speculative companies.

These are typically small market capitalization

companies that are young and growing rapidly.

They have a future that is less clear than that of

older companies, and arbitrage costs are higher.

High sentiment regarding speculative companies

is associated with low excess returns.

Even a simple sentiment indicator such as a

company’s appearance on the cover of a

business magazine can provide a signal. On

average, positive magazine cover stories follow

strong stock price performance and negative

stories follow weak stock price performance. The

researchers studying the topic conclude that

“positive stories generally indicate the end of

superior performance and negative news

generally indicates the end of poor

performance.”40

The Wisdom (and Madness) of Crowds. We now

turn to the issue of when and how the wisdom of

crowds, where markets are efficient, transitions to

the madness of crowds, where markets are

inefficient. This may be the most important

recurring behavioral opportunity.

-3

-2

-1

0

1

2

3

4

5

6

7

8

Exc

ess

Re

turn

(Pe

rce

nta

ge

Po

ints

)

Before

Firing

Before

HiringAfter

Firing

After

Hiring


For a crowd to be wise, the members need to

have heterogeneous views. To be more formal,

consider the diversity prediction theorem, which

says that given a crowd of predictive models, the

collective error equals the average individual

error minus the prediction diversity.41 You can

think of “collective error” as the wisdom of the

crowd, “average individual error” as smarts, and

“prediction diversity” as the difference among

predictive models. In markets, price veers from

value when investors come to believe the same

thing, or act as if they do. In other words, when

investors lose diversity markets lose efficiency.

One way to animate the concept is to examine

an agent-based model. These models create

agents in silico, endow them with decision rules

and objectives, allow them to interact with one

another, and provide them with the ability to

learn and adapt.

Blake LeBaron, a professor of economics at

Brandeis University and an expert in agent-based

modeling, built such a model.42 He included 1,000

agents with well-defined objectives for portfolio

allocations, a risk-free asset, an asset that pays a

dividend at a rate calibrated to the empirical

record in the last half century, and 250 active

decision rules. The agents made or lost money as

they traded and he eliminated those with the

lowest levels of wealth. He also evolved the

decision rules by removing those the agents did

not use and replacing them with new ones. The

beauty of LeBaron’s model is we can observe the

interaction between diversity and asset prices.

LeBaron’s model replicates many of the empirical

features of markets, including clustered volatility,

variable trading volumes, and fat tails. For the

purpose of this discussion, the crucial observation

is that sharp rises in the asset price are preceded

by a reduction in the number of rules the traders

used (see exhibit 5). LeBaron describes it this

way:43

During the run-up to a crash, population

diversity falls. Agents begin to use very similar

trading strategies as their common good

performance begins to self-reinforce. This

makes the population very brittle, in that a

small reduction in the demand for shares

could have a strong destabilizing impact on

the market. The economic mechanism here is

clear. Traders have a hard time finding

anyone to sell to in a falling market since

everyone else is following very similar

strategies. In the Walrasian setup used here,

this forces the price to drop by a large

magnitude to clear the market. The

population homogeneity translates into a

reduction in market liquidity.

Because the traders were using the same rules,

diversity dropped and they pushed the asset

price into bubble territory. At the same time, the

market’s fragility rose.


Exhibit 5: Agent-Based Model of Asset Prices

Source: Blake LeBaron, “Financial Market Efficiency in a Coevolutionary Environment,” Proceedings of the Workshop

on Simulation of Social Agents: Architectures and Institutions, Argonne National Laboratory and University of

Chicago, October 2000, Argonne 2001, 33-51.

The model underscores some important lessons

about behavioral inefficiency. The first is that as

the agents lose diversity by imitating one another,

the initial impact is that they get richer. This is why

betting against a bubble is so hard. Positive

feedback pushes price away from value and

creates lots of paper gains along the way. Being

wrong in the short term, even if you are correct in

the long term, introduces career risk where poor

results put a portfolio manager’s job in

jeopardy.44

Second, the market’s reaction to a reduction in

diversity is non-linear. As diversity falls, the

market’s fragility rises. But the higher asset price

obscures the underlying vulnerability. At a critical

point, however, an incremental reduction in

diversity leads to a large drop in the asset price.

Crowded trades work until they don’t.45

Crowding not only induces mispricing, it also

creates a lack of liquidity.46 When the buyers are

using the same rule and the population of sellers

using different rules has nothing left to sell, the

model reveals that the price has to drop sharply

to clear the market. The non-linear relationship

between diversity and price makes the sharp

decline appear shocking in retrospect.

How Beliefs Spread. The final lesson is how investor

beliefs come to be correlated. There is a large

body of research on this topic, but at the core

you need to understand a model of how ideas or

information propagate across a network.47

Epidemiologists use a model to describe the

spread of disease that is analogous to the spread

of beliefs, including fads and fashions.48 The

model considers the degree of contagiousness,

the degree of interaction, and the degree of

recovery. The model’s output is intuitive. The

higher the contagiousness and interaction, the

higher the likelihood that a disease or belief will

spread.

Investors attempting to assess belief propagation

in markets need to bear in mind a few points.

First, it is inherently difficult to anticipate which

ideas or products will be popular.49 For example,

film and music studios struggle to create hits.

Second, humans are inherently social and most

have a desire to conform to the crowd’s beliefs.


Scientists even have a sense of the

neurobiological basis for conformity.50

Informational cascades occur when individuals

follow the decisions of those who precede them

without regard to their personal information. For a

fad or fashion, conforming means you won’t

stand out in a way that makes you

uncomfortable.

In markets, diversity breakdowns often include

both new investors participating and seasoned

investors sitting it out. These new investors are

commonly individuals.51 When individuals buy an

investment or investment theme, the probability

of a diversity breakdown rises. The dot-com boom

and Bitcoin are two good illustrations. Likewise,

when seasoned investors stop betting against the

investment or investment theme, they contribute

to the lack of diversity. With no countervailing

opinion voting in the market, decision rules

converge and diversity suffers.

But asset markets have an additional element: If

you join the crowd early enough in the belief that

an asset price is going up and buy accordingly,

your wealth initially increases. This reinforces the

notion that you made a good decision. The asset

price influences you and makes you richer, which

feels good. Until it doesn’t.

Finally, investors feel the pressure to conform. The

CFA Institute surveyed more than 700 investors

and found that “being influenced by peers to

follow trends” was the behavioral bias that

affected decision making the most.52 It is difficult

to beat your peers if you are doing the exact

same thing that they are doing.

How does an investor effectively take advantage

of behavioral inefficiencies?

Be mindful of sentiment and

overextrapolation. Using Graham’s

metaphor, Mr. Market is generally

reasonable and price is roughly equivalent

to value. But Mr. Market is prone to

extremes. When sentiment is uniformly

positive or negative, be prepared to visit the

opposite side of the argument. But being a

contrarian for the sake of being a contrarian

is a bad idea, and the consensus can be

correct.

Rely on valuation. When markets go to

extremes, valuations tend to follow. The

crucial question is, “What expectations for

future financial results are implied by the

current price?”53 When sentiment shifts are

excessive, expectations become unduly

high or low. Do the math. Figure out what

you have to believe to justify the prevailing

price, and compare that to plausible

scenarios.

Lean on facts. When an asset price is under

the spell of extreme sentiment, make an

effort to explicitly separate facts from

opinions. A fact is information that is

presumed to have objective reality and

therefore can be disproved. An opinion is a

belief that is more than an impression but

does not meet the standard of positive

knowledge. As a result, an opinion may be

difficult to disprove. Both facts and opinions

are useful for investors, but facts should rule

the day.54

Timing. Behavioral inefficiencies can have

different time cycles. For example,

momentum tends to reverse over a

relatively short period of time of less than a

year. Large bubbles can take years to burst.

A number of prominent value investors,

including Julian Robertson at Tiger

Management, closed their funds following

the dot-com boom in the late 1990s. The

main point is that taking advantage of

behavioral inefficiencies can take more

time than investment managers perceive

they can afford.

Benjamin Graham offered what might be the

best advice. He said, “Have the courage of your

knowledge and experience. If you have formed

a conclusion from the facts and if you know your

judgment is sound, act on it—even though others

may hesitate or differ. (You are neither right nor

wrong because the crowd disagrees with you.

You are right because your data and reasoning

are right.)”55


Analytical Ineff iciencies

An analytical inefficiency arises when all

participants have the same, or very similar,

information and one investor can analyze it

better than the others can. Financial and non-

financial information include items such as

analyst earnings estimates and revisions,

management forecasts, earnings management,

sentiment, and insider trades.56 An analytical

edge versus other investors can arise from having

more analytical skill, weighing information

differently, updating views more effectively,

operating on a different time scale, or

anticipating a change in the market’s narrative.

Analytical Skill. The game of tennis provides an

analogy for understanding analytical skill.57

Imagine a match between a tennis professional

and a weekend warrior. They use the same

equipment, play on the same court, and abide

by the same rules. But the professional will have a

better technique and strategy and will be prone

to fewer errors. In the world of investing,

institutions are the professionals and individuals

are the weekend warriors.

Institutional investors generally beat individual

investors when they go head-to-head, which

means that individuals can be a good source of

excess returns for institutions. A comprehensive

survey of the behavior of individual investors

noted that “the evidence indicates that the

average individual investor underperforms the

market—both before and after fees.”58

For the market as a whole, excess positive and

negative returns must sum to zero before fees.

The magnitude of positive and negative returns is

associated with differential skill. A comprehensive

study of all of the investors in Taiwan revealed

that institutions earned abnormal excess returns

of 1.5 percentage points while individuals lost 3.8

percentage points (see exhibit 6).59 The

individuals suffered from a lack of skill and an

excess of confidence.

Institutions generally have better information and

analytical skills than individuals do. For example,

institutions tend to buy stocks from individuals in

cases when the stock underreacts to good news

about future cash flows, outperforming individuals

by 1.4 percentage points per year in these

cases.60 In addition, initial public offerings with

high participation rates by retail investors

underperform those dominated by institutions.61

Exhibit 6: When Institutions Compete with

Individuals, Institutions Tend to Win

Source: Source: Brad M. Barber, Yi-Tsung Lee, Yu-Jane

Liu, and Terrance Odean, “Just How Much Do Individual

Investors Lose by Trading?” Review of Financial Studies,

Vol. 2, No. 2, February 2009, 609-632.

Note: Returns are after commissions and transaction

taxes and before fees.

Information Weighting. Another source of

analytical edge exists when one investor has the

same information as other investors but weighs

the information differently. One simple analogy is

sizing in portfolio construction. We each construct

a portfolio using the exact same list of stocks. We

have the same information. Our returns over time

will be the result of how we weigh the positions.

What distinguishes us is not what we have to work

with but how we use what we have.

Our conviction in a particular hypothesis

combines two types of evidence. The first is the

strength, or extremeness, of the evidence, and

the second is the weight, or predictive validity.

For instance, say you have a hypothesis that a

coin is biased in favor of tails. The ratio of flips that

land on tails to those that land on heads

indicates strength, and the number of flips, or

sample size, reflects the weight.62

There are formal rules for how to combine

strength and weight correctly. But most people

do not follow the theory. In particular, the

strength of evidence tends to loom larger in

decisions than the weight of evidence. As a

result, a pattern of over- and underconfidence

emerges (see exhibit 7).

-4

-3

-2

-1

0

1

2

Institutions

Annual Abnormal

Return (Percentage

Points)

Individuals


Exhibit 7: Trade-Off between Signal Weight and

Strength

Strength

(Extremeness)

Low High

Weight

(Predictive

Validity)

Low Not yet

relevant

Over-

confidence

High Under-

confidence Obvious

Source: Dale Griffin and Amos Tversky, “The Weighing of

Evidence and the Determinants of Confidence,”

Cognitive Psychology, Vol. 24, No. 3, July 1992, 411-435.

When the strength is high and the weight is low,

people tend to be overconfident. Continuing

with the example of the coin toss, this would be

the case when tails shows up 7 times in the first 10

flips (which will happen 12 percent of the time

with a fair coin). The strength is high but the

weight is low. This mechanism is consistent with

the overconfidence and overextrapolation

discussed in the behavioral section.

In particular, you should be very alert to the risk of

overreacting to outcomes based on small sample

sizes and a related concept, recency bias, which

is the result of placing too much weight on recent

events.63 Surveys of investors and executives

consistently show a strong inclination to

incorrectly expect the near-term future to be

similar to the recent past.

When strength is low and the weight is high,

people tend to be under-confident. In this case,

the signal is faint but meaningful because of the

large sample size. It’s one thing to have 7 tails out

of 10 flips and another thing altogether to have

5,100 or more tails out of 10,000 flips (which will

happen only about 2 percent of the time with a

fair coin). The way to avoid this mistake is to

consider base rates, or the results of an

appropriate reference class.64

Be a Good Bayesian. The next source of

analytical edge is updating your views better

than others. At issue is how well you integrate

new information with your prior beliefs. The proper

way to do this is to use Bayes’s Theorem. The

theorem tells you the probability that a theory or

belief is true conditional on some event

happening. But the truth is even people who

know the theorem rarely apply it formally. What’s

essential is to be open to new information and to

be willing to change your mind.

The primary reason that we fail to sufficiently

update our beliefs in light of new information is

that we suffer from confirmation bias. The bias

manifests in a couple of ways. We tend to seek

information that confirms our belief and dismiss or

discount information that disconfirms it. Further,

we generally interpret ambiguous information in

a way that is consistent with our prior belief. Once

we believe something, the mistakes we make

often serve to preserve our view.65

Phil Tetlock, a professor of psychology at the

University of Pennsylvania, raises some other

common mistakes. One mistake is overreacting

to information that superficially appears to

explain causality but in fact does not. You see this

in the analysis of merger and acquisition (M&A)

deals. For example, equity analysts sometimes

upgrade a stock following the announcement of

an acquisition as the result of anticipated

earnings accretion, only to see the stock drop. A

change in earnings is not the best way to capture

causality in M&A.

Overreaction to new information can also be the

result of the contrast effect. The idea is that good

news is perceived as more impressive than it

should be if it is preceded by bad news, and less

impressive than it should be if it follows good

news. These are errors in perception that lead to

mispricing, and a strategy to capture the contrast

effect appears to generate excess returns.66

Another mistake is for the decision maker to

underreact to information that he or she fails to

recognize as causal. Continuing with the theme

of M&A, meaningful but underappreciated

information includes a comparison of the present

value of synergies with the premium pledged. This

requires some modest calculations but is

demonstrably more relevant than earnings

changes based on accounting figures. Decision

makers who are able to distinguish between what

information matters and what doesn’t have an

analytical edge.

Time Arbitrage. As far back as the 1970s, Jack

Treynor, an economist and luminary in the

investment industry, discussed the idea that an

investor can gain an edge by operating on a

different timescale than others. Treynor

distinguished:


between two kinds of investment ideas: (a)

those whose implications are straightforward

and obvious, take relatively little special

expertise to evaluate, and consequently

travel quickly (e.g., “hot stocks”); and (b)

those that require reflection, judgment,

special expertise, etc., for their evaluation,

and consequently travel slowly . . . Pursuit of

the second kind of idea . . . is, of course, the

only meaningful definition of “long-term

investing.”67

To explain why this opportunity exists, Treynor

refers to John Maynard Keynes, the renowned

economist, who adds two essential elements to

the case. Keynes suggests,

The energies and skill of the professional

investor . . . are, in fact, largely concerned,

not with making superior long-term forecasts

of the probable yield of an investment over

its whole life, but with foreseeing changes in

the conventional basis of valuation a short

time ahead of the general public. They are

concerned, not with what an investment is

really worth to a man who buys it “for keeps”,

but with what the market will value it at,

under the influence of mass psychology,

three months or a year hence.

He goes on to emphasize how challenging it is to

be a long-term investor:

Finally it is the long-term investor, he who

most promotes the public interest, who will in

practice come in for most criticism, wherever

investment funds are managed by

committees or boards or banks. For it is in the

essence of his behaviour that he should be

eccentric, unconventional and rash in the

eyes of average opinion. If he is successful,

that will only confirm the general belief in his

rashness; and if in the short run he is

unsuccessful, which is very likely, he will not

receive much mercy. Worldly wisdom

teaches that it is better for reputation to fail

conventionally than to succeed

unconventionally.68

Both Treynor and Keynes emphasize the

importance of time horizon and suggest that

outsized returns are available to the long-term

investor. But they make clear that long-term

investing requires “reflection and judgment” and

that those who practice it will “come in for most

criticism.” This is a blend of analytical and

behavioral issues.

Investors use the term “time arbitrage” to reflect

cases where the market reflects short-term noise

as if it were long-term signal. Returning to the

example of the coin toss, an opportunity for time

arbitrage exists if the market prices a fair coin as if

it is biased after 7 of the first 10 flips are tails.

There are three elements to successfully taking

advantage of time arbitrage. The first is that the

investor must be able to accurately separate

signal from noise. In the coin toss example, the

signal is an even split between tails and heads,

and the noise is the appearance of a bias toward

tails. The second is the signal must eventually

reveal itself. That is, after lots of flips, the ratio of

tails to heads settles very close to one-to-one (see

exhibit 8). The third is you must have access to

capital that is sufficiently patient to allow the

results to materialize.

Exhibit 8: A Simple Model of Time Arbitrage


0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10

Pe

rce

nta

ge

Th

at

Are

Ta

ils

Number of Trials

0

10

20

30

40

50

60

70

80

90

100

1 100 10,000

Pe

rce

nta

ge

Th

at

Are

Ta

ils

Number of Trials (Log)


There are two related reasons opportunities arise

with regard to time horizon. The first is a concept

called “myopic loss aversion,” developed by the

economists Shlomo Benartzi and Richard Thaler.

Benartzi and Thaler tried to address the empirical

puzzle of why the equity risk premium, the

premium for owning stocks versus less-risky bonds,

is higher than theory would suggest.69

Benartzi and Thaler attempt to explain the

historical equity risk premium by combining two

ideas. The first is loss aversion, which says humans

suffer losses roughly twice as much as they enjoy

equivalent gains.70 That you should be twice as

upset at losing $100 as you are happy at winning

$100 is inconsistent with classical utility theory.

The second idea is myopia, which means

“nearsightedness.” This reflects how frequently

you look at your investment portfolio. The stock

market tends to go up over time, but it rises by fits

and starts. Based on nearly a century of data, the

probability you will see a gain in your diversified

U.S. stock portfolio is roughly 51 percent for a day,

53 percent for a week, and 75 percent for a year.

Look out a decade or more and the probability

of a profit is very close to 100 percent.

Both ideas are well established on their own, but

together they address the issue of investor time

horizon in a new way. The more frequently an

investor looks at his or her portfolio, the more likely

he or she is to observe losses and suffer from loss

aversion. As a result, an investor examining his or

her portfolio all the time requires a higher return

to compensate for suffering from losses than one

who looks at his or her portfolio infrequently and

hence suffers less. A long-term investor is willing to

pay a higher price for the same asset than is a

short-term investor.71 Evidence from the field

suggests that professional investors are not

immune from myopic loss aversion.72

The portfolio evaluation period consistent with the

realized equity risk premium from 1926 through

1990 was about one year. Investors may not be

able to select their degree of loss aversion, but

they can select how frequently they evaluate

their portfolios. Using a simulation technique

grounded in realistic parameters, researchers at

the investment firm Renaissance Technologies

found that the evaluation period that worked

best was longer than three years. They summarize

their analysis by noting that “the most profitable

degree of patience is very different from that

found in current industry practice.”73

The second idea related to opportunity and time

horizon is that we differ in our degrees of loss

aversion, and the degree we suffer changes as

the result of recent experience. You can gain an

analytical edge by making consistent decisions

with regard to the opportunity set.

One fascinating experiment showed how hard

that is to do.74 Researchers created a simple

investment game and drew players from two

groups: patients with brain damage and ordinary

subjects. The patients with brain damage had

normal intelligence, and no harm was done to

the regions of their brains that handled logic and

cognitive reasoning. The regions of the brain that

were harmed controlled emotions, including the

usual ability to experience fear or anxiety. They

didn’t suffer after they lost.

The researchers gave each participant $20, and

the game consisted of 20 rounds of coin tosses.

For each round, individuals could play or sit out. If

they played, they passed $1 to the experimenter

who flipped a fair coin and paid $2.50 for tails

and nothing for heads. The subjects got to keep

their dollar if they sat out the round. The objective

was to end up with as much money as possible.

The game is not hard to figure out. The expected

value of handing $1 to the experimenter is $1.25,

higher than the value of keeping it. The ideal

strategy is to invest in every round. All of the

participants appeared to grasp the basic math.

But the patients with brain damage ended up

with 13 percent more money, on average, than

the normal patients did. The difference was the

subjects with brain damage participated in 45

percent more rounds than did the players in the

control group. In particular, the brain-damaged

subjects played roughly 80 percent of the rounds

after having lost compared to the 40 percent

played by ordinary participants (see exhibit 9).

Nearly all of the players participated in the early

rounds. But an interesting pattern emerged.

Normal players appeared to suffer from loss

aversion after they lost a round and sat out

subsequent rounds at a higher rate than the

patients with brain damage who did not suffer

from loss aversion. Loss aversion caused normal

participants to forgo a positive expected value

bet after having lost when a heads appeared on

a toss. Even though the financial proposition was

consistently attractive, the recent experience of

the normal participants colored their actions.75


Exhibit 9: Loss Aversion Leads to Suboptimal Decision Making

Brain-damaged subjects make more money . . . because they play more rounds.

Source: Baba Shiv, George Loewenstein, Antoine Bechara, Hanna Damasio, and Antonio R. Damasio, “Investment

Behavior and the Negative Side of Emotion,” Psychological Science, Vol. 16, No. 6, June 2005, 435-439.

Here is a final thought on long-term investing.

Gathering information and reflecting it in stock

prices is a costly endeavor. Long-term investing

allows a shareholder to amortize that cost over

an extended holding period. As Cliff Asness,

founder, Managing Principal, and Chief

Investment Officer at AQR Capital Management,

has said, “Having, and sticking to, a true long

term perspective is the closest you can come to

possessing an investing super power [sic].”76

The Power of Stories. The concluding possible

source of analytical edge is anticipating how the

narrative about a company will change, leading

to a revision in the valuation the market accords

the stock. The change in a stock price over time

reflects a change in expectations. Fundamental

results, including sales growth and profits, exert a

large influence in shaping expectations. But the

stories that investors tell, and believe, also play a

meaningful role in revisions of expectations.77

Psychologists have shown that “alternative

descriptions of the same event often produce

systematically different judgments.”78 A debate

between Aswath Damodaran, a professor of

finance at the Stern School of Business at New

York University and a recognized expert in

valuation, and Bill Gurley, a leading venture

capitalist at Benchmark Capital, serves as a good

example. Both are believers in valuing businesses

using a discounted cash flow model.

In June 2014, Damodaran suggested a valuation

for Uber, an online transportation network, of $5.9

billion. His analysis came on the heels of a round

of fundraising that valued the company at $17

billion. In July 2014, Gurley, whose firm was an

early investor, responded with a piece called

“How to Miss By a Mile,” suggesting that

Damodaran considered a total addressable

market that was too small.79 At the heart of their

disagreement was a description: the professor

thought Uber was going after the global taxi and

car-service market and the venture capitalist

assumed vastly more cases for using Uber,

including replacing the need to own a car.

At the end of the day, the value of a company’s

stock is the cash it distributes to its shareholders

over the company’s life. But a company’s stock

price along the way can contribute to the

company’s reputation, capacity to raise capital,

and ability to pay employees with equity.

Damodaran and Gurley agreed on the tools of

analysis but differed on the narrative to drive the

analysis.

0

2

4

6

8

10

12

14

16

18

20

22

24

26

Ave

rag

e A

mo

un

t Ea

rne

d (

Do

llars

)

Brain-

Damaged

Normal0

10

20

30

40

50

60

70

80

90

100

Pe

rce

tna

ge

of

Ro

un

ds

Pla

ye

d

Brain-

Damaged

Normal



of analytical inefficiencies?

Find easy games. The idea is to find

situations where you have more analytical

skill than your competitors. We highlighted

the case of institutions competing against

individuals. Research shows that “dumb

money” creates market anomalies that the

“smart money” can correct.80

Weight available information effectively. Be

mindful of how you combine the strength, or

extremeness, of a signal with its weight, or

predictive value. We tend to fall for the

recency bias, placing too much weight on

recent events and not enough weight on a

fuller series of results. The key is to learn how

to blend the inside view, our assessment

based on our own circumstances and

experience, with the outside view, the

outcomes for the appropriate reference

class.

Update effectively. Once we have made up

our mind, the confirmation bias often blocks

our ability to update our views when new

information arrives. And even when we

incorporate new information, we commonly

under- or overestimate its significance. One

means to deal with this is to write down the

signposts you expect to see, including

probabilities, if your thesis unfolds as you

expect. Use those signposts to examine

whether your thesis remains intact or you

have to change your mind.

Make time your friend. An investment

process can be tailored to a long- or short-

term holding period. Jack Treynor argued

that “slow traveling” ideas, those that

require reflection, judgment, and special

expertise, are the impetus for long-term

investing. Taking a long view is difficult

because of client pressures and career risk.

Indeed, stress encourages us to shorten our

time horizon and can lead us to suffer even

more because of loss aversion.81

Recognize the power of stories. We know

that different descriptions can lead to

different decisions. Insight into how the story

about a company may change over time

will allow you to anticipate material

changes in valuation. Reported

fundamentals matter, but so do the stories

that the investment community tells.82


Informational Inefficiencies

An information inefficiency arises when some

market participants have different information

than others and can trade profitably on that

asymmetry. As Grossman and Stiglitz pointed out,

gathering information relevant to value can be

expensive and investors who do so can

reasonably expect to earn excess returns. But

regulation has ensured that companies disclose

and disseminate information uniformly, and

technology makes it quick and cheap to do so.

As a consequence, the cost of gathering legal,

non-traditional information has escalated.

An informational edge can take a few forms. The

first is to legally acquire relevant information that

others don’t have. Second, there is substantial

evidence that attention is costly and that some

inefficiencies arise from limited attention as a

result. Paying attention to the right information

can provide edge. Finally, there is research that

shows that complexity slows the process of

information diffusion, so anticipating the impact

of information can confer edge.

Find Out First. The first and most obvious source of

informational edge is to know things relevant to

value that others don’t yet know. It is useful to

distinguish between data and information. Data

is the plural of datum, which means “something

given.” Data need not be useful. Information

organizes data in a way that is useful. Technically,

information reduces uncertainty. Access to data

does not confer an informational edge. An ability

to translate data into information, or access to

information directly, can be a source of edge.

This source of edge may be linked to size and

scale. Bigger investment firms can amortize the

cost of data and have the ability to turn it into

information more cost effectively than smaller

firms can.

There is no doubt that some investors generate

excess returns by acquiring information that other

investors don’t have. For example, some hedge

funds made lots of money by using the Freedom

of Information Act to collect non-public

information about pharmaceutical companies

from the U.S. Food and Drug Administration.83 You

can imagine a host of innovative, if costly, means

to acquire useful information, including hiring top

law firms to interpret legal issues, working with

consultants to grasp political dynamics, or

engaging a firm that specializes in analyzing

storm damage to assess the potential costs. There

has been an explosion in data gathering, from

credit card receipts to satellite images counting

cars in parking lots, which has created an arms

race in the investment community.

The mosaic theory, which the legendary investor

Phil Fisher called “scuttlebutt,” describes an

approach that gathers public and non-public

information from a variety of sources, including

suppliers, competitors, customers, and former

employees in an attempt to create an edge.84

The value in the approach relies not on a single

piece of information but rather on how various

pieces of information combine to form a view

that is different from that of the market. The

synthesis of information is more important than

any one bit of information.

Consistent with Grossman and Stiglitz, there is a

high cost and benefit of information gathering.

Investors who can translate information into asset

prices benefit by earning excess returns. Society

benefits by having asset prices that are more

efficient.

Regulation has focused on making access to

corporate information uniform. Regulation Fair

Disclosure (Reg FD) was implemented in October

2000 in the United States. Reg FD prohibits

companies “from privately disclosing material

information to select investors or securities

markets professionals without simultaneously

disclosing the same information to the public.”85

On balance, the evidence shows the

implementation of Reg FD led to greater

informational efficiency and that some

investment firms that had previously benefited

from privileged disclosure lost an informational

edge.86

There is illuminating evidence of Reg FD’s effect

on market efficiency. When enacted in 2000,

credit rating agencies were exempt from the

regulation. That meant credit analysts had

access to confidential information unavailable to

equity analysts. In 2010, the Dodd-Frank

legislation repealed that exemption. The research

shows that the informational effect of bond rating

upgrades and downgrades was greater after

Reg FD than before it, and that the effect faded

after the credit agencies lost access to that

privileged information in 2010.87


Another source of asymmetric information that

leads to wealth transfers is the repurchase and

issuance of equity by corporations. Generally

speaking, firms buy back stock when it is

undervalued and issue stock when it is

overvalued, which benefits ongoing shareholders

at the expense of the shareholders who sell or

buy. Companies are able to do this because

executives have better information about the

company’s prospects than investors do. Speaking

to the relevance of this information asymmetry

and the potential for excess returns, the

economists who did this research suggest that

“these wealth transfers can be predicted using a

variety of firm characteristics and that future

wealth transfers are an important determinant of

current stock prices.”88

Pay Attention. On Sunday, May 3, 1998, the New

York Times published an article on the front page

about a potential breakthrough in cancer

treatment via an injection of drugs that halt the

blood supply to tumors. The article mentioned

EntreMed, a company that had the licensing

rights to the technology.89 The next day, the stock

skyrocketed from around $12 to more than $51

per share on volume 78 times the daily average.

The elevated stock price persisted through the

rest of the year.

What makes this story remarkable is that the

science magazine, Nature, and the New York

Times ran stories covering the substance of this

research in late 1997.90 There was no new news.

This brings us to our second source of

informational edge: paying attention.91 There is

substantial evidence that investors have limited

attention and hence do not incorporate all

available information. This presents opportunity to

investors who can assimilate relevant information.

One simple model is based on the fraction of

investors who are inattentive. When that fraction

is very low, markets tend to be informationally

efficient. When that fraction is high, asset prices

fail to reflect available information and an

opportunity for a variant perception arises.

Research in psychology suggests that a few

factors determine the fraction of inattentive

investors, including the salience of the

information, the resources investors use to address

the information, and how easily investors can

process the information. In general, investors

have a harder time paying attention the more

stimuli they face that competes for that attention.

Information can get lost in the shuffle.

You can think of the process of making

investment decisions in two parts. The first relates

to attention and the second relates to

preferences. Researchers found that individual

investors, in particular, are drawn to stocks that

grab attention. For example, stocks that host Jim

Cramer recommends on the television show Mad

Money enjoy large short-term gains.92 As a result,

investment decisions can be more sensitive to the

choice of what investors pay attention to than to

their preferences. This effect is much less

pronounced with institutional investors, who have

more time and can allocate their attention with

more rigor.93

Task Complexity. Task complexity is the final

source of informational edge. As Lee and So

summarize in their survey paper, “Signal

complexity impedes the speed of market price

adjustment.” The idea is that the market takes

longer to digest new information when the

implications are not obvious than when they are

obvious.

To study this concept, financial economists

examined how new information about an

industry affected companies with a single

business versus conglomerates with multiple

businesses. The authors provide a hypothetical

example of new information that reveals that

chocolate consumption improves longevity. The

stock price of a company that is in the chocolate

business only would react more quickly than the

stock of a conglomerate that has a fraction of its

business in chocolate. The economists “find

strong evidence that easy-to-analyze firms

incorporate industry information first, and hence

their returns strongly predict the future updating

of firm values that require more complicated

analyses.”94

Another case of task complexity is how the

market reacts to information related to trading

partners in a supply chain. In cases when two

companies have businesses that are related, for

example one supplies the other, the market

reflects new information about the first company

into the stock of the second company with a lag.

Investors can generate excess returns purchasing

shares of the supplier following the release of

positive news about its customer.95



of informational inefficiencies?

Gather legal information that others do not

have. This source of edge is very difficult to

attain and is potentially very costly.

Capturing information that the market has

yet to digest produces excess returns for the

investor and creates a benefit for society in

the form of more efficient prices. A related

idea is to capture lots of weak signals that,

when combined, generate a strong signal.96

Recognize that not all information is

immediately reflected in prices. Investors

have limited attention, and hence

information that is relevant to value is not

always immediately expressed in stock

prices. Information that garners lots of

attention tends to be assimilated more

readily than information that is more subtle.

The less direct the impact, the slower the

market may be to reflect information. While

it is generally reasonable to assume that

information is rapidly reflected in prices,

evidence shows that the market is less

efficient at incorporating information if the

task of doing so is complex. Informational

edge may arise from seeing the implication

of new information on parts of the market

where the impact is not obvious

immediately.


Technical Inefficiencies

A technical inefficiency arises when some market

participants have to buy or sell securities for

reasons that are unrelated to fundamental value.

Laws, regulations, contracts, and internal policies

may impose rules that shape the actions of

certain institutions. These actions may make sense

for an individual firm but can create inefficiency.

Further, some trades are prompted by limits,

requirements, or constraints that the buyer or

seller cannot avoid.

Here is a simple example. William Sharpe, an

economist who won the Nobel Prize in 1990,

wrote a paper about active management that

advances two basic arguments. The first is that

the return on the average dollar managed

actively will equal that of a dollar managed

passively before costs. The second is that the

return on the average dollar managed actively

will be less than that of a dollar managed

passively after costs.97

Sharpe’s analysis, while practical, ignores the fact

that index funds have to buy and sell securities in

order to reflect actions including stock sales and

purchases by companies, as well as inclusions

and deletions from the index. The annual cost of

this trading is estimated to be as low as 20 basis

points for funds that track well-known equity

indexes and can be larger for bond funds.98 A

similar argument applies to the arbitrage costs of

keeping the price equal to the net asset value for

exchange-traded funds.99 We need active

managers to take the other side of these trades.

There are a few examples of opportunity for

technical edge. Importantly, each case requires

access to capital. One is to be on the other side

of forced sellers or buyers. For example, some

investors receive margin calls following a

drawdown and have to sell assets. Second is to

consider the opposite side of securities perturbed

by investor fund flows. In this case, investment

managers have to buy or sell securities and do so

with a predictable pattern. The final opportunity is

to step in when traditional arbitrageurs have

limited access to capital and hence fail to fulfill

their normal function.

Forced Buyers or Sellers. A straightforward

example of forced sellers is insurance companies

that must own investment-grade bonds. Indeed,

many insurance companies have portfolios that

look similar. Regulatory requirements compel

insurance companies to sell bonds that the credit

agencies downgrade from investment grade to

high yield. Research shows that these fire sales

lead to an increase in yield spreads beyond what

the fundamentals justify. This creates a temporary

mispricing, which tends to get corrected within

months of the event.100

The leverage cycle, developed by John

Geanakoplos, a professor of economics at Yale

University, provides a useful framework for

understanding forced selling. Central to

Geanakoplos’s argument is that to understand

booms and crashes, the ability to borrow is more

important than the level of interest rates.101

One measure of the ability to borrow is the

amount of debt, relative to equity, a buyer can

access to buy an asset. When the equity amount

is low and the debt amount is high, the ability to

borrow is easy. Leverage availability is

procyclical. It tends to be easier to borrow after

asset prices are up and harder to borrow after

they are down.

Geanakoplos argues that some buyers place a

higher value on an asset than others for a host of

potential reasons, including more optimism and

higher risk tolerance. These optimists use debt to

bid up asset prices when leverage is easily

accessible.

Optimistic buyers who have bid up asset prices

fueled by debt create a setup for a crash. First,

asset prices drop because of bad news, which

heightens volatility, uncertainty, and

disagreement. The price decrease commonly

follows a sharp price increase.

The initial drop in asset prices triggers a big

decline in the wealth of optimistic asset owners.

The drawdown in asset prices forces the optimists

to sell assets to meet their margin requirements.

This leads to additional declines in asset values,

which triggers further selling, and so forth. These

optimistic asset owners are selling for reasons that

are unrelated to their view of fundamental value.

Before prices find a new equilibrium, lenders

make borrowing harder by requiring owners to

put up more equity against their assets. This wipes

out some buyers, leaving even fewer investors to

support asset prices. Spillovers, when owners in

one asset class cover their losses by selling assets

in other classes, become a risk. Investors who


survive or can step in have a technical edge and

can exploit an attractive opportunity.

The leverage cycle shows that asset prices can

drop meaningfully below fair value because of

forced selling stemming from margin calls and

more stringent margin requirements. This is a fire

sale.102 This creates a technical edge for investors

who can take the other side of the sale.

The Importance of Fund Flows. Technical edge

can also arise from funds buying or selling specific

securities as a result of investor inflows or outflows.

Here’s the basic outline.103 Investors tend to give

money to investment funds that have done well

and withdraw money from investment funds that

have done poorly. Investment managers who

receive additional capital tend to buy the

securities they already own, and those who face

withdrawals have to sell securities in the portfolio.

As a result, positive flows tend to create positive

price pressure and negative flows create

negative price pressure. These effects are

particularly pronounced for securities that are

hard to trade and hence have a high cost of

liquidity. This adds or detracts from fund results in

the short run, but the price effects reverse within

months or in some cases years. One analysis

concludes that one-third of hedge fund excess

return, or alpha, is attributable to investor flows.104

One of the ways we can think about “Who is on

the other side?” is by sorting between trades

induced by fundamental value and those

induced by liquidity. Stocks bought or sold for

fundamental reasons reveal “stock-picking skill,”

while stocks traded for liquidity reasons exhibit

“negative performance effects.”105 There is some

evidence that sophisticated funds already take

advantage of this technical inefficiency.106

When Arbitrageurs Fail to Show Up. A technical

inefficiency can also arise when arbitrageurs

have insufficient capital to close gaps between

price and value. With pure arbitrage, an investor

buys and sells identical assets that have different

prices, say gold in London and New York, and

locks in a profit. There is no risk and no capital

needed. These opportunities are scarce. With risk

arbitrage, an investor buys and sells assets but is

not assured a profit, hence the introduction of

“risk.”107 Arbitrageurs are plentiful in the

investment community, and under normal

conditions they have sufficient capital to

profitably remove divergences between price

and value. But they do not have unlimited

capital.

Professional arbitrageurs are generally agents.

Their capital comes from principals such as

wealthy individuals, endowments, or pension

funds. They negotiate with prime brokers to set

the amount and cost of leverage they can use.

History shows that both principals and lenders

retrench in instances of extreme stress, and

meaningful arbitrage opportunities persist. This

can create advantage.

Long-Term Capital Management (LTCM) is a case

study that incorporates many of the sources of

inefficiency we have discussed. Founded in 1994,

LTCM enjoyed compound annual returns in

excess of 30 percent in its first 4 years, but

effectively went bust in 1998. The demise was the

result of exposure to Russia, which devalued its

currency and defaulted on its debt, in August

1998, and losses in highly leveraged positions,

among other issues. A consortium of banks bailed

out the fund and eventually liquidated it at a

modest profit.

One aspect of LTCM’s troubles that tends to get

short shrift is the degree to which other funds and

banks mimicked the fund’s positions. Consistent

with Blake LeBaron’s agent-based model, other

financial institutions copied LTCM’s trades, hence

making them crowded and increasing the

market’s fragility. Also similar to LeBaron’s model,

imitation was a benefit to returns early on but

made finding new profitable trades more difficult.

In July 1998, Sandy Weill, then CEO of the

Travelers Group, decided to shut down the U.S.

arbitrage desk of Salomon Brothers. Travelers had

acquired Salomon in the fall of 1997 and had just

agreed to merge with Citicorp. Salomon decided

to let a separate group within the firm liquidate

the arbitrage book, which meant that the

process of unwinding the positions was quicker

and lost more money than would normally be the

case. This created stress for LTCM and other firms

that had similar trades.108

Leverage also played a role in LTCM’s demise.

The leverage ratio at the firm was 27-to-1 in early

1998, which means the firm borrowed roughly $96

to finance an investment of $100. Many of LTCM’s

positions demanded and justified high leverage

in order to generate satisfactory returns, and that

leverage ratio was equivalent to the average of

the five largest investment banks at the time.


Substantial leverage can make sense for

convergence trades that have low risk and are

part of a well-diversified portfolio. Using five-year

historical data, the correlation between LTCM’s

positions was less than 0.10 through early 1998

(where zero means there is no correlation at all

and 1.0 means perfect correlation). To stress test

the portfolio, LTCM’s risk managers assumed the

correlations could reach 0.30, a figure they

deemed improbable. The correlation skyrocketed

to 0.70 as the crisis unfolded, rendering traditional

risk management tools essentially useless.109

By early September 1998, after having lost 44

percent of its capital in August, LTCM sent out a

communication to its clients that the opportunity

set looked unusually attractive. The missive

immediately became public. Rather than having

the intended outcome of securing additional

capital, it created additional pressure on LTCM’s

positions and sparked concern among

counterparties. In so doing, LTCM is also a vivid

example of the leverage cycle.

The story of LTCM reveals how technical

inefficiencies arise as the result of a lack of well-

capitalized arbitrageurs. While the conditions

were extreme, these episodes occur in markets

from time to time. Access to capital is key to the

ability to take advantage of these chances.

Before leaving the topic of technical

inefficiencies, it is worth mentioning two other

areas: spin-offs and the impact on stock prices of

adding and removing stocks from prominent

indexes. Both have been sources of opportunity

historically, but they appear to be more muted

opportunities today. It is worth watching each,

especially spin-offs, for potential technical edge.

A spin-off is the result of a distribution of shares of

a wholly-owned subsidiary to a parent

company’s shareholders on a pro-rata and tax-

free basis. Joel Greenblatt, founder of Gotham

Capital, explains that the opportunity for

technical edge arises because, “Once the

spinoff’s shares are distributed to the parent

company’s shareholders, they are typically sold

immediately without regard to price or

fundamental value.”110

Historically, spin-offs have created value on

average for the companies spun off as well as

the parents.111 One meta-analysis of the literature

on spin-offs summarized their findings by saying:

“The main conclusion is consistent: spin-offs are

associated with strongly significant abnormal

returns.” Factors that contribute to this value

creation include sharpened corporate focus,

better information for investors, enhanced merger

and acquisition opportunities, and in some cases

tax treatment.112

As good of an investment opportunity that spin-

offs have been historically, they have not

delivered as much value in recent years. For

example, the Invesco S&P Spin-Off ETF (CSD) has

lagged the S&P 500 by about 17 percentage

points cumulatively in the 2 years ended

December 2018. That said, spin-offs potentially

combine analytical, informational, and technical

inefficiencies and are worth monitoring in the

search for edge.

In classic economic theory, demand curves for

stocks are close to flat by means of arbitrage

between perfect substitutes. Because there are

few perfect substitutes in the world, stocks and

other assets are subject to demand shocks. If the

demand curve shifts up, the stock price has to rise

to clear the market.

Demand shocks can have a meaningful impact

on asset price valuation. For instance, financial

economists showed that the institutionalization of

investment management in the 1980s and 1990s

led to demand for large capitalization stocks. By

their estimate, this demand contributed 230 basis

points to the aggregate 260 basis point

outperformance of large capitalization versus

small capitalization stocks from 1980 through 1996

(see exhibit 10).113

Exhibit 10: Demand Shock and Large Cap versus

Small Cap Performance (1980-1996)

Source: Paul A. Gompers and Andrew Metrick,

“Institutional Investors and Equity Prices,” Quarterly

Journal of Economics, Vol. 116, No. 1, February 2001,

229-259.

Demand-

Based Return

0

2

4

6

8

10

12

14

16

Small Cap Large Cap

An

nu

aliz

ed

To

tal R

etu

rn

(Pe

rce

nt)


One of the ways that financial economists study

demand curves for stocks is through inclusions

and deletions from prominent indexes such as the

S&P 500. Inclusion in the index, for example,

creates demand from index funds with no

change in the fundamentals of the company. This

is a textbook case of a technical inefficiency,

where index funds have to buy for reasons that

have nothing to do with value.

The substantial body of research on this topic

shows that demand curves slope down and as a

result inclusion into an index has a positive, if

temporary, impact on the stock price.114

However, more recent work suggests that effect

has become much more muted in the past

decade, reflecting greater transparency and

lower transaction costs.115 Index funds have to

buy and sell in order to be aligned with the index

itself. But the big index funds do this very

efficiently.


of technical inefficiencies?

Be on the lookout for forced sellers. From

time to time, certain market participants are

compelled to buy or sell securities without

regard for fundamental value. The

unwinding of the leverage cycle, where

optimistic buyers have to sell as the result of

margin calls, is a good example.

Watch investor flows and the buying and

selling that results. The basic story is that

inflows are the result of good short-term

returns and that managers who receive

inflows generally buy more of what they

own, which itself creates a near-term boost.

Outflows generally follow poor

performance, and managers have to sell

what they own.

Seek situations where arbitrageurs are

stretched. Under normal conditions,

arbitrageurs do a very good job of aligning

price and value. But from time to time,

arbitrageurs lack access to the capital they

need to close gaps between price and

value.

Keep an eye on spin-offs. For a long time,

spin-offs have been a great illustration of a

technical inefficiency. But in recent years,

perhaps as a result of the publication of

these results, spin-offs have not fared as well.

Still, we believe it is worthwhile to evaluate

spin-offs as they are announced to see if an

informational or analytical opportunity exists.


Summary

Markets cannot be fully informationally efficient

because there is a cost to gather information and

reflect it in asset prices. The degree of efficiency is

a function of how difficult it is to acquire

information and the friction associated with

buying and selling securities to capture value.

There is a continuum of efficiency across

countries and asset classes. Exhibit 11 summarizes

some of the qualitative determinants of

efficiency, most of which are discussed in the

report.

There are quantitative methods to assess the

degree of efficiency, including a measure of

entropy, or lack of predictability.116 David

Swenson, chief investment officer of the Yale

endowment, proposes a simpler measure based

on the distribution of returns for active

managers.117 His notion is intuitive: asset classes

that have a wide dispersion of returns for active

managers tend to have more opportunities, and

hence are less efficient, than those that have

narrow dispersion. Exhibit 12 shows dispersion

based on annual returns, net of fees, for a dozen

asset classes. Dispersion is highest for venture

capital and lowest for taxable bond portfolios.

Exhibit 11: Market Efficiency Continuum

Less efficient More efficient

Limited analyst coverage Lots of analyst coverage

Information is complex Information is straightforward

Low investor diversity (crowded) High investor diversity

Market extrapolates noise Market reflects signal

Recent market extreme (fear or greed) Neutral market conditions

Forced buyers or sellers Natural flow of buyers and sellers

Few substitutes Lots of substitutes

Constrained ability to short Easy to short

Costly to finance Cheap to finance

Arbitrageurs have limited access to capital Arbitrageurs well financed


Exhibit 12: Dispersion of Returns for Active Managers in Various Asset Classes

Source: Preqin and Morningstar Direct.

Note: L/S=long/short; Calculations for private equity investments based on net internal rate of return since-inception

for vintage years 2000-2015; Calculations for hedge funds and mutual funds based on trailing 5-year annualized

returns through 12/31/2018 using returns net of expenses with income reinvested.

-30%

-20%

-10%

0%

10%

20%

30%

Venture

Capital

Buyout Real

Estate

Distressed

Debt

Global

Macro

L/S Equity

Emerging

Markets

L/S Equity

US/

Global

L/S

Debt

Multi-

strategy

US Large

Cap

Equity

US Small/

Mid Cap

Equity

US

Taxable

Bond

Dis

pe

rsio

n f

rom

th

e M

ed

ian

Private Equity Hedge Funds Mutual Funds

95th

5th

75th

25th

Percentiles


We categorize market inefficiencies in four areas:

behavioral, analytical, informational, and

technical. There is overlap between these

categories. Behavioral inefficiencies are likely the

most enduring because human nature has not

changed much over time and is unlikely to

change much in the future. Behavioral

inefficiencies are also among the most difficult to

capture because of our individual tendencies to

stick with the crowd and due to pressure from

clients during inevitable periods of

underperformance.

To generate excess returns, investors should be

skillful and seek easy games.118 In investing as in

poker, the key to winning is participating in a

game where there is differential skill and you are

the most skilled player. This is a challenge

because markets are generally highly

competitive, low-skill games are often small, and

agency costs commonly compel the wrong

behaviors.

The main goal of this report is to encourage a

good answer to the question of “Who is on the

other side?” A further objective is to understand

how an investment firm is organized, including its

alignment with clients, so as to have the greatest

opportunity to take advantage of pockets of

inefficiency.


Checklist for Identifying Market Inefficiencies

Are investors overextrapolat ing f rom recent resu l ts , leading to unreal i s t ic

expectat ions?

I s there evidence of performance chasing in a secur i ty , sector , or asset c lass?

Do sent iment indicator s suggest extreme fear or greed?

Do investors have corre lated views that create f ragi l i ty in the market?

Do you have a di f ferent t ime hor i zon, a l lowing you to take advantage of t ime

arbi t rage?

Are you more analyt ical ly sk i l l fu l than the other investor s you are competing

wi th?

Are you p lacing di f ferent , and more preci se , weights on information?

Are you accurate ly updating your views based on new information?

Do you have reason to bel ieve that the s tory about a secur i ty wi l l change?

Do you unders tand a complex investment oppor tuni ty better than others?

Have you legal ly acqui red information that other investor s don’t have?

Are you paying attent ion to a l l re levant information?

Are you trading wi th forced buyers or se l lers?

Can you take the other s ide of fund f lows?

Can you step in when arbi t rageurs are tapped out?


Appendix A: Agency Theory in Asset Management

In the 2001 address to the American Finance

Association, Franklin Allen, a professor emeritus of

finance at the Wharton School at the University of

Pennsylvania, drew attention to a puzzling

dichotomy: the role of institutions is central to the

study of corporate finance but is nearly absent in

the study of asset pricing.119 For example,

researchers have extensively studied agency

theory, which considers potential conflicts

between principals (owners) and agents (those

who make decisions on behalf of principals), in

corporate finance for decades. Yet agency

theory is rarely used in academic work on asset

pricing, including the CAPM model and the

Black-Scholes options pricing model.

There is an edifying distinction in asset

management between the business of investing

and the profession of investing.120 The business of

investing dwells on generating profit for the

investment firm, often by growing assets under

management and charging healthy fees. The

profession of investing focuses on managing

portfolios to maximize long-term, risk-adjusted

returns. Of course, a vibrant business is essential to

the profession if only to attract and retain talent.

Agency costs arise when an investment firm

prioritizes the business over the profession.

Pointing out this principal-agent problem may be

useful, but it says little about actual asset pricing.

There are a couple of plausible reasons that the

principal-agent problem and financial institutions

do not play a large role in asset-pricing models.

The first is that there was not much of a principal-

agent problem as asset pricing theory

developed. For instance, individuals directly

owned more than 90 percent of equities in the

United States in 1950 and still owned just under 50

percent in 1980.121 At the time that core theories

about asset pricing were established in the 1960s

and 1970s, financial institutions were simply not

the dominant factor that they are today. A lack

of agents led to a lack of agency theory for asset

pricing.

The other reason reflects the theoretical

foundations for the efficient market hypothesis.122

There are essentially three ways to get to efficient

markets. The first is to assume that investors are

rational, which means they understand their

preferences and the distribution of asset price

returns, and they know how to make an optimal

trade-off between risk and reward.

No one believes investors can actually do this,

but it may be a useful model to the extent the

market behaves as if this is what is going on.

Milton Friedman, who was a professor of

economics at the University of Chicago and won

the Nobel Prize in 1976, used the analogy of an

expert billiards player. His point is that the billiards

player is certainly not solving “complicated

mathematical formulas” to sink her shots but you

can make good predictions about her results by

assuming that she does.123 The market’s empirical

results, especially at extremes, undermines this

argument.

Another way to get to efficient markets is to

assume that a smart subset of investors,

arbitrageurs, cruise markets and close gaps

between price and value. The allure of

arbitrageurs is that we can relax the assumption

that all investors are rational and retain a

plausible mechanism to attain efficiency. As we

have seen, arbitrageurs are active in markets but

have failed to ensure efficiency at key times.124

The final way to achieve efficiency is through the

wisdom of crowds. Here, a price that is equivalent

to value is the result of an effective aggregation

of the views of a group of investors who are

cognitively diverse and have appropriate

incentives.125 This approach does not work when

the key conditions are not in place.126

Each case takes for granted the institutions and

mechanisms on the path to efficiency.127 And in

each case, we now know that the institutions

make a big difference. Many of the gaps

between theory and practice are the source of

the inefficiencies that we describe in this report.


Appendix B: Factors—Risk or Behavioral?

One lively debate in finance is whether the

excess returns of certain factors, relative to the

capital asset pricing model, reflect risk or

mispricing due to behavioral issues. Practitioners

cannot viably use most of the 450-plus such

anomalies that finance researchers have

identified and many that they can implement are

less robust than the research suggests.128 The

ability to implement and robustness are essential

standards for empirical finance.

Within this “factor zoo,”129 six factors are widely

used in the investment community, including

beta (measured though the capital asset pricing

model),130 size (small capitalization stocks

generate higher returns than large capitalization

stocks), value (low-multiple stocks outperform

high-multiple ones),131 momentum (stocks that rise

continue to rise in the short term),132 quality (high-

quality companies outperform low-quality

companies),133 and asset growth (low asset

growth companies outperform high asset growth

companies).134 Eugene Fama and Kenneth

French, a professor of finance at the Tuck School

of Business at Dartmouth College, recommend a

five-factor model that includes all of the above

save momentum.135

The critical question is whether the excess returns

these factors imply reflect risk or a combination of

arbitrage costs and behavioral mistakes by

investors.136 If the returns are the result of risk the

CAPM misses, the factors are useful for capturing

that risk. This brings you back to efficient markets,

where your long-term returns as an investor are

commensurate with the risk that you accept.

The answer is that the excess returns from factors

reflect both risk and behavioral mistakes. But

some may be more behaviorally-oriented than

others. Andrew Ang, a former professor of finance

at Columbia Business School and now the head

of factor investing strategies at BlackRock, a

large asset management firm, recommends

asking whether a factor works based on whether

it rewards risk, takes advantage of a structural

impediment, or capitalizes on behavioral

biases.137

While explaining the exact source of excess

returns for any factor is inherently difficult, solid

evidence suggests that the value,138

momentum,139 and quality140 factors have a large

dose of behavioral influence. Risk appears to be

the main driver of excess returns for the CAPM

and size factors.

The source of excess return from a given factor is

relevant for answering the question of who is on

the other side of a trade. If excess returns relative

to the CAPM’s predictions reflect risk, then the

factors are helpful in making sure you are

receiving proper compensation. If excess returns

reflect behavioral issues, they suggest a source of

returns that are both extra and recurring. But the

sums that winners earn must be offset by the sums

that losers surrender.


Endnotes

1 Jeffrey Wurgler, “Financial Markets and the Allocation of Capital,” Journal of Financial Economics,

Vol. 58, Nos. 1-2, December 2000, 187-214 and Jules H. van Binsbergen and Christian Opp, “Real

Anomalies,” Journal of Finance, forthcoming. 2 See Alex Hutchinson, Endure: Mind, Body, and the Curiously Elastic Limits of Human Performance (New

York: HarperCollins, 2018), 143. The loss of energy is dictated by the second law of thermodynamics. 3 Eugene F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of

Finance, Vol. 25, No. 2, May 1970, 383-417. Also, see Ronald J. Gilson and Reinier H. Kraakman, “The

Mechanisms of Market Efficiency,” Virginia Law Review, Vol. 70, No. 4, May, 1984, 549-644 and Burton

G. Malkiel, “Reflections on the Efficient Market Hypothesis: 30 Years Later,” Financial Review, Vol. 40, No.

1, February 2005, 1-9. 4 Much of the discussion in this report is informed by this excellent survey paper: Charles M.C. Lee and

Eric So, “Alphanomics: The Informational Underpinnings of Market Efficiency,” Foundations and Trends

in Accounting, Vol. 9, No. 2-3, 2014, 59-258. Also, Andrew Ang, William N. Goetzmann, and Stephen M.

Schaefer, “The Efficient Market Theory and Evidence: Implications for Active Investment

Management,” Foundations and Trends in Accounting, Vol. 5, No. 3, 2010, 157-242. 5 Sanford J. Grossman and Joseph E. Stiglitz, “On the Impossibility of Informationally Efficient Markets,”

American Economic Review, Vol. 70, No. 3, June 1980, 393-408. 6 Lasse Heje Pedersen, Efficiently Inefficient: How Smart Money Invests and Market Prices Are

Determined (Princeton, NJ: Princeton University Press, 2015) and Nicolae Gârleanu and Lasse Heje

Pedersen,” Efficiently Inefficient Markets for Assets and Asset Management,” Journal of Finance, Vol.

73, No. 4, August 2018, 1663-1712. 7 Lee and So, 175-206; Owen A. Lamont and Richard H. Thaler, “Anomalies: The Law of One Price in

Financial Markets,” Journal of Economic Perspectives, Vol. 17, No. 4, Fall 2003, 191-202; Ĺuboš Pástor

and Robert F. Stambaugh, “Liquidity Risk and Expected Stock Returns,” Journal of Political Economy,

Vol. 111, No. 3, June 2003, 642-685; Yongqiang Chu, David A. Hirshleifer, and Liang Ma, “The Causal

Effect of Limits to Arbitrage on Asset Pricing Anomalies,” SSRN Working Paper, July 24, 2018; and

Andrew J. Patton and Brian M. Weller, “What You See Is Not What You Get: The Costs of Trading Market

Anomalies,” Economic Research Initiatives at Duke (ERID) Working Paper No. 255, November 28, 2018.

For analysis that suggests that trading costs are lower than previous studies suggest, see Andrea

Frazzini, Ronen Israel, and Tobias J. Moskowitz, “Trading Costs,” SSRN Working Paper, April 7, 2018. 8 Robert J. Shiller, “Stock Prices and Social Dynamics,” Brookings Papers on Economic Activity, Vol. 2,

1984, 457-510. 9 Fischer Black, “Noise,” Journal of Finance, Vol. 41, No. 3, July 1986, 529-543. 10 Ibid., 533. On a separate note, Richard Thaler, a professor of economics at the University of Chicago

and a Nobel Prize winner in 2017, tells the story of the Herzfeld Caribbean Basin Fund, a closed-end

mutual fund with the ticker symbol “CUBA” that had about 70 percent of its assets in U.S. stocks and

most of the rest in Mexican stocks. In the months leading up to December 18, 2014, CUBA traded at a

10-15 percent discount to net asset value (NAV), which is common for a closed-end fund. On

December 18, 2014, President Obama announced his intention to improve the U.S.’s diplomatic

relations with Cuba. Even though CUBA the fund had nothing to do with Cuba the country, the fund

skyrocketed to a 70 percent premium. While smaller, a premium persisted for nearly another year. It is

hard to justify such price moves in an efficient market. See Richard H. Thaler, “Behavioral Economics:

Past, Present, and Future,” American Economic Review, Vol. 106, No. 7, July 2016, 1577-1600. 11 Eugene F. Fama, “Efficient Capital Markets: II,” Journal of Finance, Vol. 46, No. 5, December 1991,

1575-1617. 12 Nicholas Barberis and Richard H. Thaler, “A Survey of Behavioral Finance” in George Constantinides,

Milton Harris, and Rene M. Stulz, eds., Handbook of the Economics of Finance (Amsterdam: Elsevier,

2003), 1053-1128. 13 John Y. Campbell, Andrew W. Lo, and A. Craig MacKinlay, The Econometrics of Financial Markets

(Princeton, NJ: Princeton University Press, 1997), 24. 14 Jack D. Schwager, Hedge Fund Market Wizards: How Winning Traders Win (Hoboken, NJ: John Wiley

& Sons, 2012), 217. 15 R. David McLean and Jeffrey Pontiff, “Does Academic Research Destroy Stock Return Predictability?”

Journal of Finance, Vol. 71, No. 1, February 2016, 5-32. For a good summary of anomalies in equity

markets, see Leonard Zacks, ed., The Handbook of Equity Market Anomalies: Translating Market

Inefficiencies into Effective Investment Strategies (Hoboken, NJ: John Wiley & Sons, 2011); Also, Heiko

Jacobs and Sebastian Müller, “Anomalies Across the Globe: Once Public, No Longer Existent,” Journal

of Financial Economics, Forthcoming, October 2018.


16 Campbell R. Harvey, Yan Liu, and Heqing Zhu, “… and the Cross-Section of Expected Returns,”

Review of Financial Studies, Vol. 29, No. 1, January 2016, 5-68. 17 Benjamin Graham, The Intelligent Investor: The Classic Text on Value Investing, Third Edition (New

York: HarperBusiness, 2005), XXIV. 18 Warren E. Buffett, “Letter to Shareholders,” Berkshire Hathaway Annual Report, 1987. See

www.berkshirehathaway.com/letters/1987.html. 19 Mark Granovetter, “Threshold Models of Collective Behavior,” American Journal of Sociology, Vol. 83,

No. 6, May, 1978, 1420-1443. 20 Bradford Cornell, “What Moves Stock Prices: Another Look,” Journal of Portfolio Management, Vol.

39, No. 3, Spring 2013, 32-38. The original study was David M. Cutler, James M. Poterba, and Lawrence

H. Summers, “What Moves Stock Prices?” Journal of Portfolio Management, Vol. 15, No. 3, Spring 1989,

4-12. 21 Clifford S. Asness, Tobias J. Moskowitz, and Lasse Heje Pedersen, “Value and Momentum

Everywhere,” Journal of Finance, Vol. 68, No. 3, June 2013, 929-985. 22 M. Keith Chen, Venkat Lakshminarayanan, and Laurie R. Santos, “How Basic Are Behavioral Biases?

Evidence from Capuchin Monkey Trading Behavior,” Journal of Political Economy, Vol. 114, No. 3, June

2006, 517-537. 23 Dhananjay Gode and Shyam Sunder, “Allocative Efficiency of Markets with Zero-Intelligence

Traders,” Journal of Political Economy, Vol. 101, No. 1, February 1993, 119-137. 24 Mark Harrison, “Unapologetic after All These Years: Eugene Fama Defends Investor Rationality and

Market Efficiency,” CFA Institute Blog, May 14, 2012. 25 Nicholas C. Barberis, “Psychology-based Models of Asset Prices and Trading Volume,” NBER Working

Paper No. 24723, June 2018. 26 Robin Greenwood and Andrei Shleifer, “Expectations of Returns and Expected Returns,” Review of

Financial Studies, Vol. 27, No. 3, March 2014, 714-746; Aleksandar Andonov and Joshua D. Rauh, “The

Return Expectations of Institutional Investors,” Stanford University Graduate School of Business Research

Paper No. 18-5, November 19, 2018; and Nicola Gennaioli and Andrei Shleifer, A Crisis of Beliefs:

Investor Psychology and Financial Fragility (Princeton, NJ: Princeton University Press, 2018). 27 John H. Cochrane, “Discount Rates,” Journal of Finance, Vol. 66, No. 4, August 2011, 1047-1108. 28 Warren E. Buffett, “Letter to Shareholders,” Berkshire Hathaway Annual Report, 2017. See

www.berkshirehathaway.com/letters/2017ltr.pdf. 29 From Seth Klarman’s speech at Columbia Business School on October 2, 2008. Reproduced in

Outstanding Investor Digest, Vol. 22, Nos. 1 & 2, March 17, 2009, 3. 30 Nicholas Barberis, Robin Greenwood, Lawrence Jin, Andrei Shleifer, “Extrapolation and Bubbles,”

Journal of Financial Economics, Vol. 129, No. 2, August 2018, 203-227 and Anna Scherbina and Bernd

Schlusche, “Asset Price Bubbles: A Survey,” Quantitative Finance, Vol. 14, No. 4, 2014, 589-604. 31 Robin Greenwood, Andrei Shleifer, and Yang You, “Bubbles for Fama,” Journal of Financial

Economics, Vol. 131, No. 1, January 2019, 20-43. 32 Victor Haghani and Samantha McBride, “Return Chasing and Trend Following:

Superficial Similarities Mask Fundamental Differences,” SSRN Working Paper, January 29, 2016.

Momentum also has meaningful reversals. See Kent Daniel, Tobias J. Moskowitz, “Momentum Crashes,”

Journal of Financial Economics, Vol. 122, No. 2, November 2016, 221-247. 33 Andrea Frazzini and Owen A. Lamont, “Dumb Money: Mutual Fund Flows and the Cross-Section of

Stock Returns,” Journal of Financial Economics, Vol. 88, No. 2, May 2008, 299-322 and Amit Goyal, Antti

Ilmanen, and David Kabiller, “Bad Habits and Good Practices,” Journal of Portfolio Management, Vol.

41, No. 4, Summer 2015, 97-107. 34 Amit Goyal and Sunil Wahal, “The Selection and Termination of Investment Management Firms by

Plan Sponsors,” Journal of Finance, Vol 63, No. 4, August 2008, 1805-1847 and Rob Arnott, Vitali Kalesnik

and Lillian Wu, “The Folly of Hiring Winners and Firing Losers,” Journal of Portfolio Management, Vol. 45,

No. 1, Fall 2018, 71-84. 35 Scott D. Stewart, John J. Neumann, Christopher R. Knittel, and Jeffrey Heisler, “Absence of Value: An

Analysis of Investment Allocation Decisions by Institutional Plan Sponsors,” Financial Analysts Journal,

Vol. 65, No. 6, November/December 2009, 34-51. 36 Don A. Moore and Derek Schatz, “The Three Faces of Overconfidence,” Social and Personality

Psychology Compass, Vol. 11, No. 8, August 2017 and J. Edward Russo and Paul J.H. Schoemaker,

“Managing Overconfidence,” Sloan Management Review, Vol. 33, No. 2, Winter 1992, 7-17. 37 Brad M. Barber and Terrance Odean, “Boys will be Boys: Gender, Overconfidence, and Common

Stock Investment,” Quarterly Journal of Economics, Vol. 116, No. 1, February 2001, 261-292 and Mark

Grinblatt and Matti Keloharju, “Sensation Seeking, Overconfidence, and Trading Activity,” Journal of

Finance, Vol. 64, No. 2, April 2009, 549-578. 38 Harrison Hong and Jeremy C. Stein, “Disagreement and the Stock Market,” Journal of Economic

Perspectives, Vol. 21, No. 2, Spring 2007, 109-128.

http://www.berkshirehathaway.com/letters/1987.html


39 Malcolm Baker and Jeffrey Wurgler, “Investor Sentiment and the Cross-Section of Stock Returns,”

Journal of Finance, Vol. 61, No. 4, August 2006, 1645-1680; Malcolm Baker and Jeffrey Wurgler, “Investor

Sentiment in the Stock Market,” Journal of Economic Perspectives, Vol. 21, No. 2, Spring, 2007, 129-151;

Dashan Huang, Fuwei Jiang, Jun Tu, and Guofu Zhou, “Investor Sentiment Aligned: A Powerful Predictor

of Stock Returns,” Review of Financial Studies, Vol. 28, No. 3, March 2015, 791-837; Todd Feldman and

Shuming Liu, “Contagious Investor Sentiment and International Markets,” Journal of Portfolio

Management, Vol. 43, No. 4, Summer 2017, 125-136; and Paul Hribar and John McInnis, “Investor

Sentiment and Analysts’ Earnings Forecast Errors,” Management Science, Vol. 58, No. 2, February 2012,

293-307. 40 Tom Arnold, John H. Earl, Jr., and David S. North, “Are Cover Stories Effective Contrarian Indicators?”

Financial Analysts Journal, Vol. 63, No. 2, March/April 2007, 70-75. 41 Scott E. Page, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and

Societies (Princeton, NJ: Princeton University Press, 2007), 208-209. 42 Blake LeBaron, “Financial Market Efficiency in a Coevolutionary Environment,” Proceedings of the

Workshop on Simulation of Social Agents: Architectures and Institutions, Argonne National Laboratory

and University of Chicago, October 2000, Argonne 2001, 33-51. 43 Ibid., 50. 44 The CFA Institute surveyed 774 investors and 78 percent said that career risk due to

underperformance is a factor at their firms. See Rebecca Fender, “The Investment Risk You’ve Never

Calculated,” Enterprising Investor: CFA Institute, June 17, 2016. 45 “The Novus 4C Indices: Conviction, Concentration, Consensus, and Crowdedness,” Novus Research,

Q2 2018. 46 Lasse Heje Pedersen, “When Everyone Runs for the Exit,” International Journal of Central Banking, Vol.

5, No. 4, December 2009, 177-199. 47 For example, see Mark Granovetter, “Threshold Models of Collective Behavior,” American Journal of

Sociology, Vol. 83, No. 6, May 1978, 1420-1443; Duncan J. Watts, “A Simple Model of Global Cascades

on Random Networks, PNAS, Vol. 99, No. 9, April 30, 2002, 5766-5771; Sushil Bikhchandani, David

Hirshleifer, and Ivo Welch, “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational

Cascades,” Journal of Political Economy, Vol. 100, No. 5, October 1992, 992-1026; Todd Feldman and

Shuming Liu, “Contagious Investor Sentiment and International Markets,” Journal of Portfolio

Management, Vol. 43, No. 4, Summer 2017, 125-136; and Bing Han, David A. Hirshleifer, and Johan

Walden, “Social Transmission Bias and Investor Behavior,” Rotman School of Management Working

Paper No. 3053655, June 19, 2018. 48 Scott E. Page, The Model Thinker: What You Need to Know to Make Data Work for You (New York:

Basic Books, 2018), 131-142. 49 Matthew J. Salganik, Peter Sheridan Dodds, and Duncan J. Watts, “Experimental Study of Inequality

and Unpredictability in an Artificial Cultural Market,” Science, Vol. 311, No. 5762, February 10, 2006, 854-

856. 50 S.E. Asch, “Effects of Group Pressure Upon the Modification and Distortion of Judgments,” in Harold

Guetzkow (ed.), Groups, Leadership and Men (Pittsburgh, PA: Carnegie Press, 1951), 177-190; Gregory

S. Berns, Jonathan Chappelow, Caroline F. Zink, Giuseppe Pagnoni, Megan E. Martin-Skurski, and Jim

Richards, “Neurobiological Correlates of Social Conformity and Independence During Mental

Rotation,” Biological Psychiatry, Vol. 58, No. 3, August 2005, 245-253; and Robert Schnuerch and

Henning Gibbons, “A Review of Neurocognitive Mechanisms of Social Conformity,” Social Psychology,

Vol. 45, No. 6, November 2014, 466-478. 51 David C. Yang and Fan Zhang, “Be Fearful When Households Are Greedy: The

Household Equity Share and Expected Market Returns,” SSRN Working Paper, August 2017. 52 Shreenivas Kunte, “The Herding Mentality: Behavioral Finance and Investor Biases,” Enterprising

Investor, August 6, 2015. 53 Alfred Rappaport and Michael J. Mauboussin, Expectations Investing: Reading Stock Prices for Better

Returns (Boston, MA: Harvard Business School Press, 2001). 54 George Soros, a billionaire investor and philanthropist, discusses this within his theory of reflexivity. He

says that reflexivity is relevant when there are thinking participants, and their thinking serves what he

calls the “cognitive” and “manipulative” function. With the cognitive function, reality shapes one’s

views; with the manipulative function, one’s views shape reality. See George Soros, “General Theory of

Reflexivity,” Financial Times, October 26, 2009. 55 Benjamin Graham, The Intelligent Investor: A Book of Practical Counsel, Fourth Revised Edition (New

York: Harper & Row, 1973), 289. 56 Mark Bradshaw, Yonca Ertimur, and Patricia O’Brien, “Financial Analysts and Their Contribution to

Well-Functioning Capital Markets,” Foundations and Trends in Accounting, Vol. 11, No. 3, 2016, 119-191. 57 Charles D. Ellis, “The Loser’s Game,” Financial Analysts Journal, Vol. 31, No. 4, July/August 1975, 19-26.


58 Brad M. Barber and Terrance Odean, “The Behavior of Individual Investors,” in George

Constantinides, Milton Harris, and Rene M. Stulz, eds., Handbook of the Economics of Finance

(Amsterdam: Elsevier, 2013), 1533-1570. 59 Brad M. Barber, Yi-Tsung Lee, Yu-Jane Liu, and Terrance Odean, “Just How Much Do Individual

Investors Lose by Trading?” Review of Financial Studies, Vol. 2, No. 2, February 2009, 609-632. These

individuals do not learn effectively. See Brad M. Barber, Yi-Tsung Lee, Yu-Jane Liu, Terrance Odean,

and Ke Zhang, “Learning Fast or Slow,” Working Paper, December 27, 2018. 60 Randolph B. Cohen, Paul A. Gompers, and Tuomo Vuolteenaho, “Who Underreacts to Cash-Flow

News? Evidence from Trading between Individuals and Institutions,” Journal of Financial Economics,

Vol. 66, Nos. 2-3, November-December 2002, 409-462. 61 Laura Casares Field and Michelle Lowry, “Institutional versus Individual Investment in IPOs: The

Importance of Firm Fundamentals,” Journal of Financial and Quantitative Analysis, Vol. 44, No. 3, June

2009, 489-516. 62 Dale Griffin and Amos Tversky, “The Weighing of Evidence and the Determinants of Confidence,”

Cognitive Psychology, Vol. 24, No. 3, July 1992, 411-435 and Cade Massey and George Wu, “Detecting

Regime Shifts: The Causes of Under- and Overreaction,” Management Science, Vol. 51, No. 6, June

2005, 932–947. 63 Amos Tversky and Daniel Kahneman, “Belief in the Law of Small Numbers,” Psychological Bulletin, Vol.

76, No. 2, August 1971, 105-110. 64 Dan Lovallo and Daniel Kahneman, “Delusions of Success: How Optimism Undermines Executives’

Decisions,” Harvard Business Review, October 2012, 46-56 and Michael J. Mauboussin, “The True

Measures of Success,” Harvard Business Review, July 2003, 56-63.

There are a few steps you can take to gain from this edge. The first is to determine which value driver is

most important for a business. One way to do this is to examine the correlation between that driver

and total shareholder returns. For most companies, the key value driver is sales growth. The correlation

between three-year sales growth rates and total shareholder returns, considering a large sample of

companies, is about 0.25.

You then want to assess the reliability of that driver. In other words, you measure the persistence of

sales growth rates. The correlation between sales growth rates from one year to the next is about 0.30.

Finally, you want to integrate the base rate. The base rate would be the actual rate of sales growth for

all comparable companies. The reliability correlation gives you a hint about how to blend the

individual company’s results with the base rate. If the correlation is low, you should place nearly all of

the weight on the base rate. These are cases when the strength is low and the weight, via the base

rate, is high.

If the correlation is high, you can emphasize the individual company’s results. This is the case where

both strength and weight are high. 65 Chetan Dave and Katherine W. Wolfe, “On Confirmation Bias and Deviations From Bayesian

Updating,” Working Paper, March 21, 2003. This happens to experts as well. See Philip E. Tetlock, Expert

Political Judgment: How Good Is It? How Can We Know? (Princeton, NJ: Princeton University Press,

2005), 122-123. 66 Samuel M. Hartzmark and Kelly Shue, “A Tough Act to Follow: Contrast Effects in Financial Markets,”

Journal of Finance, Vol. 73, No. 4, August 2018, 1567-1613. 67 Jack L. Treynor, “Long-Term Investing,” Financial Analysts Journal, May/June 1976, 56-59. 68 John Maynard Keynes, The General Theory of Employment, Interest and Money (New York: Harcourt

Brace Jovanovich, Inc., 1936), 154-158. 69 Shlomo Benartzi and Richard H. Thaler, “Myopic Loss Aversion and the Equity Premium Puzzle,”

Quarterly Journal of Economics, Vol. 110, No. 1, February 1995, 73-92. 70 Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision under Risk,”

Econometrica, Vol. 47, No. 2, March 1979, 263-292 and John W. Payne, Suzanne B. Shu, Elizabeth C.

Webb, and Namika Sagara, “Development of an Individual Measure of Loss Aversion,” Association for

Consumer Research, October 3, 2015. 71 Alternatively, long-term investors earn higher returns because they are willing to take on more risk.

See Boram Lee and Yulia Veld-Merkoulova, “Myopic Loss Aversion and Stock Investments: An Empirical

Study of Private Investors,” Journal of Banking & Finance, Vol. 70, September 2016, 235-246. 72 Michael S. Haigh and John A. List, “Do Professional Traders Exhibit Myopic Loss Aversion? An

Experimental Analysis,” Journal of Finance, Vol. 60, No. 1, February 2005, 523-534 and Francis Larson,

John A. List, Robert D. Metcalfe, “Can Myopic Loss Aversion Explain the Equity Premium Puzzle?

Evidence from a Natural Field Experiment with Professional Traders,” NBER Working Paper No. 22605,

September 2016. 73 David L. Donoho, Robert A. Crenian, and Matthew H. Scanlan, “Is Patience a Virtue? The

Unsentimental Case for the Long View in Evaluating Returns,” Journal of Portfolio Management, Vol. 37,

No. 1, Fall 2010, 105-120.


74 Baba Shiv, George Loewenstein, Antoine Bechara, Hanna Damasio, and Antonio R. Damasio,

“Investment Behavior and the Negative Side of Emotion,” Psychological Science, Vol. 16, No. 6, June

2005, 435-439. 75 Alex Imas, “The Realization Effect: Risk-Taking after Realized versus Paper Losses,”

American Economic Review, Vol. 106, N0. 8, August 2016, 2086-2109. 76 From a tweet dated January 27, 2017. Also, see Chunhua Lan, Fabio Moneta, and Russell R. Wermers,

“Holding Horizon: A New Measure of Active Investment Management,” American Finance Association

Meetings 2015 Paper, July 1, 2018. 77 Aswath Damodaran, Narrative and Numbers: The Value of Stories in Business (New York: Columbia

Business School Publishing, 2017). 78 Amos Tversky and Derek J. Koehler, “Support Theory: A Nonextensional Representation of Subjective

Probability,” Psychological Review, Vol. 101, No. 4, October 1994, 547-567. 79 Bill Gurley, “How to Miss By a Mile: An Alternative Look at Uber’s Potential Market Size,” Above the

Crowd, July 11, 2014. 80 Ferhat Akbas, Will J. Armstrong, Sorin Sorescu, Avanidhar Subrahmanyam, “Smart Money, Dumb

Money, and Capital Market Anomalies,” Journal of Financial Economics, Vol. 118, No. 2, November

2015, 355-382. 81 Robert M. Sapolsky, Why Zebras Don’t Get Ulcers: An Updated Guide to Stress, Stress-Related Disease,

and Coping (New York: W. H. Freeman and Company, 1994) and Michael J. Mauboussin, More Than

You Know: Finding Financial Wisdom in Unconventional Places, Updated and Expanded (New York:

Columbia Business School Publishing, 2008), 71-76. 82 Jonathan Gottschall, The Storytelling Animal: How Stories Make Us Human (New York: Houghton

Mifflin Harcourt, 2012). 83 Antonio Gargano, Alberto G. Rossi, and Russ Wermers, “The Freedom of Information Act and the

Race Toward Information Acquisition,” Review of Financial Studies, Vol. 30, No. 6, June 2017, 2179-2228. 84 Philip A. Fisher, Common Stocks and Uncommon Profits (New York: Harper & Brothers, 1958). For a

good discussion of information intensity and mutual fund returns, see George Jiang, Ke Shen, Russ

Wermers, and Tong Yao, “Costly Information Production, Information Intensity, and Mutual Fund

Performance,” SSRN Working Paper, November 21, 2018. 85 Frank Heflin, K. R. Subramanyam, and Yuan Zhang, “Regulation FD and the Financial Information

Environment: Early Evidence,” Accounting Review, Vol. 78, No. 1, January 2003, 1-37. 86 Sanjeev Bhojraj, Young Jun Cho, and Nir Yehuda, “Mutual Fund Size, Fund Family Size and Mutual

Fund Performance: The Role of Regulatory Changes,” Journal of Accounting Research, Vol. 50, No. 3,

June 2012, 647-684. 87 Philippe Jorion, Zhu Liu, and Charles Shi, “Informational Effects of Regulation FD: Evidence from

Rating Agencies,” Journal of Financial Economics, Vol. 76, No. 2, May 2005, 309-330 and Louis H.

Ederington, Jeremy Goh, Yen Teik Lee, and Lisa Yang, “Are Bond Ratings Informative? Evidence from

Regulatory Regime Changes,” Research Collection Lee Kong Chian School of Business, May 2018, 1-33. 88 Richard G. Sloan and Haifeng You, “Wealth Transfers via Equity Transactions,” Journal of Financial

Economics, Vol. 118, No. 1, October 2015, 93-112. 89 Gina Kolata, “Hope in the Lab: A Cautious Awe Greets Drugs That Eradicate Tumors in Mice,” New

York Times, May 3, 1998. EntreMed changed its name to CASI Pharmaceuticals and still trades. 90 Gur Huberman and Tomer Regev, “Contagious Speculation and a Cure for Cancer: A Nonevent that

Made Stock Prices Soar,” Journal of Finance, Vol. 56, No. 1, February 2001, 387-396. 91 Sonya S. Lim and Siew Hong Teoh, “Limited Attention,” in H. Kent Baker and John R. Nofsinger, eds.,

Behavioral Finance: Investors, Corporations, and Markets (Hoboken, NJ: John Wiley & Sons, 2010), 295-

312; David Hirshleifer and Siew Hong Teoh, “Limited Attention, Information Disclosure, and Financial

Reporting,” Journal of Accounting and Economics, Vol. 36, No. 1-3, December 2003, 337-386; and Bin

Miao, Siew Hong Teoh, and Zinan Zhu, “Limited Attention, Statement of Cash Flow Disclosure, and the

Valuation of Accruals,” Review of Accounting Studies, Vol. 21, No. 2, June 2016, 473-515. Research

shows that a combination of a short-term factor based on earnings surprise and a long-term model

based on equity issuance or retirement generates excess returns. See Kent Daniel, David Hirshleifer,

and Lin Sun, “Short- and Long-Horizon Behavioral Factors,” Columbia Business School Research Paper

No. 18-5, December 29, 2018. 92 Joseph Engelberg, Caroline Sasseville, and Jared Williams, “Market Madness? The Case of ‘Mad

Money,’” Management Science, Vol. 58, No. 2, February 2012, 351-364. 93 Brad M. Barber and Terrance Odean, “All That Glitters: The Effect of Attention and News on the

Buying Behavior of Individual and Institutional Investors,” Review of Financial Studies, Vol. 21, No. 2,

March 2008, 785-818 and Mark S. Seasholes and Guojun Wu, “Predictable Behavior, Profits, and

Attention,” Journal of Empirical Finance, Vol. 14, No. 5, December 2007, 590-610. 94 Lauren Cohen and Dong Lou, “Complicated Firms,” Journal of Financial Economics,

Vol. 104, No. 2, May 2012, 383-400.


95 Lauren Cohen and Andrea Frazzini,” Economic Links and Predictable Returns,” Journal of Finance,

Vol. 63, No. 4, August 2008, 1977-2011; Anna D. Scherbina, and Bernd Schlusche, “Follow the Leader:

Using the Stock Market to Uncover Information Flows Between Firms,” Review of Finance, forthcoming;

and Ling Cen, Michael G. Hertzel; Christoph Schiller, “Speed Matters: Limited Attention and Supply-

Chain Information Diffusion,” SSRN Working Paper, January 25, 2018; and Lauren Cohen, Christopher

Malloy, Quoc Nguyen, “Lazy Prices,” NBER Working Paper No. 25084, September 2018. 96 Paul J.H. Schoemaker and George S. Day, “How to Make Sense of Weak Signals,” MIT Sloan

Management Review, Vol. 50, No. 3, Spring 2009, 80-89. 97 William F. Sharpe, “The Arithmetic of Active Management,” Financial Analysts Journal, Vol. 47, No. 1,

January/February 1991, 7-9. 98 Lasse Heje Pedersen, “Sharpening the Arithmetic of Active Management,” Financial Analysts Journal,

Vol. 74, No. 1, First Quarter 2018, 21-36. 99 Antti Petajisto, “Inefficiencies in the Pricing of Exchange-Traded Funds,” Financial Analysts Journal,

Spring 2017, 24-54. 100 Andrew Ellul, Chotibhak Jotikasthira, and Christian T. Lundblad, “Regulatory Pressure and Fire Sales in

the Corporate Bond Market,” Journal of Financial Economics, Vol. 101, No. 3, September 2011, 596-620;

Vikram Nanda, Wei Wu, and Xing Zhou, “Investment Commonality across Insurance Companies: Fire

Sale Risk and Corporate Yield Spreads,” Finance and Economics Discussion Series 2017-069,

Washington: Board of Governors of the Federal Reserve System, June 2017; and Giovanni Cespa and

Thierry Foucault, “Illiquidity Contagion and Liquidity Crashes,” Review of Financial Studies, Vol. 27, No. 6,

June 2014, 1615-1660. 101 John Geanakoplos, “The Leverage Cycle,” Cowles Foundation Discussion Paper No.1715R, January

2010. 102 Andrei Shleifer and Robert Vishny, “Fire Sales in Finance and Macroeconomics,” Journal of

Economic Perspectives, Vol. 25, No. 1, Winter 2011, 29-48. 103 Joshua Coval and Erik Stafford, “Asset Fire Sales (and Purchases) in Equity Markets,” Journal of

Financial Economics, Vol. 86, No. 2, November 2007, 479-512; Dong Lou, “A Flow-Based Explanation for

Return Predictability,” Review of Financial Studies, Vol. 25, No. 12, December 2012, 3457-3489; and

Geoffrey C. Friesen and Travis R. A. Sapp, “Mutual Fund Flows and Investor Returns: An Empirical

Examination of Fund Investor Timing Ability,” Journal of Banking & Finance, Vol. 31, No. 9, September

2007, 2796-2816. 104 Katja Ahoniemi and Petri Jylhä, “Flows, Price Pressure, and Hedge Fund Returns,” Financial Analysts

Journal, Vol. 70, No. 5, September/October 2014, 73-93. 105 Martin Rohleder, Dominik Schulte, Janik Syryca, and Marco Wilkens, “Mutual Fund Stock‐Picking Skill:

New Evidence from Valuation‐ versus Liquidity‐Motivated Trading,” Financial Management, Vol. 47, No.

2, Summer 2018, 309-347. 106 Joseph Chen, Samuel Hanson, Harrison Hong, and Jeremy C. Stein, “Do Hedge Funds Profit from

Mutual-Fund Distress?” NBER Working Paper 13786, February 2008. 107 Andrei Shleifer and Robert W. Vishny, “The Limits of Arbitrage,” Journal of Finance, Vol. 52, No. 1,

March 1997, 35-55. 108 MacKenzie, 2003. For a more general discussion of crowding and leverage, see Jeremy C. Stein,

“Presidential Address: Sophisticated Investors and Market Efficiency,” Journal of Finance, Vol. 64, No. 4,

August 2009, 1517-1548. 109 Donald MacKenzie, An Engine, Not a Camera: How Financial Models Shape Markets

(Cambridge, MA: MIT Press, 2006), 233. 110 Joel Greenblatt, You Can Be a Stock Market Genius (Even if you’re not too smart!): Uncover the

Secret Hiding Places of Stock Market Profits (New York: Fireside, 1997), 61.

111 Patrick J. Cusatis, James A. Miles, and Randall Woolridge, “Restructuring through Spinoffs: The Stock

Market Evidence,” Journal of Financial Economics, Vol. 33, No. 3, June 1993, 293-311; Hemang Desai

and Prem C. Jain, “Firm Performance and Focus: Long-Run Stock Market Performance Following

Spinoffs,” Journal of Financial Economics, Vol. 54, No. 1, October 1999, 75-101; John J. McConnell and

Alexei V. Ovtchinnikov, “Predictability of Long-Term Spinoff Returns,” Journal of Investment

Management, Vol. 2, No. 3, Third Quarter 2004, 35-44; Marc Zenner, Evan Junek, and Ram Chivukula,

“Shrinking to Grow: Evolving Trends in Corporate Spin-offs,” Journal of Applied Corporate Finance, Vol.

27, No. 3, Summer 2015, 131-136; John J. McConnell, Steven E. Sibley, and Wei Xu, “The Stock Price

Performance of Spin-Off Subsidiaries, Their Parents, and the Spin-Off ETF, 2001-2013,” Journal of Portfolio

Management, Fall 2015, 143-152; and Li Ma and Temi Oyeniyi, “Capital Market Implications of Spinoffs,”

S&P Global Quantamental Research, March 2017. 112 Chris Veld and Yulia V. Veld-Merkoulova, “Value Creation through Spinoffs: A Review of the

Empirical Evidence,” International Journal of Management Reviews, Vol. 11, No. 4, December 2009,

407-420 and Mieszko Mazur, “Creating M&A Opportunities through Corporate Spin-Offs,” Journal of

Applied Corporate Finance, Vol. 27, No. 3, Summer 2015, 137-143.


113 Andrei Shleifer, “Do Demand Curves for Stocks Slope Down?” Journal of Finance,

Vol. 41, No. 3, July 1986, 579-590; Philip A. Cusick, “Price Effects of Addition or Deletion from the

Standard & Poor's 500 Index: Evidence of Increasing Market Efficiency,” Financial Markets, Institutions

and Instruments, Vol. 11, No. 4, November 2002, 349-383; Jeffrey Wurgler and Ekaterina Zhuravskaya,

“Does Arbitrage Flatten Demand Curves for Stocks?” Journal of Business, Vol. 75, No. 4 October 2002,

583-608; Honghui Chen, Gregory Noronha, and Vijay Singal, “The Price Response to S&P 500 Index

Additions and Deletions: Evidence of Asymmetry and a New Explanation,” Journal of Finance, Vol. 59,

No. 4, August 2004, 1901-1930; William B. Elliott, Bonnie F. Van Ness, Mark D. Walker, and Richard S. Warr,

“What Drives the S&P 500 Inclusion Effect? An Analytical Survey,” Financial Management, Vol. 35, No. 4,

December 2006, 31-48; and Antti Petajisto, “Why Do Demand Curves for Stocks Slope Down?” Journal

of Financial and Quantitative Analysis, Vol. 44, No. 5, October 2009, 1013-1044. 114 Paul A. Gompers and Andrew Metrick, “Institutional Investors and Equity Prices,” Quarterly Journal of

Economics, Vol. 116, No. 1, February 2001, 229-259. 115 Konstantina Kappou, “The Diminished Effect of Index Rebalances,” Journal of Asset Management,

Vol. 19, No. 4, July 2018, 235-244. 116 Luciano Zuninoa, Massimiliano Zanin, Benjamin M. Tabak, Darío G. Pérez, and Osvaldo A. Rosso,

“Complexity-Entropy Causality Plane: A Useful Approach to Quantify the Stock Market Inefficiency,”

Physica A, Vol. 389, No. 9, May 1, 2010, 1891-1901. 117 David F. Swensen, Pioneering Portfolio Management: An Unconventional Approach to Institutional

Management (New York: Free Press, 2000), 74-79. 118 Gerard Hoberg, Nitin Kumar, and Nagpurnanand Prabhala, “Mutual Fund Competition, Managerial

Skill, and Alpha Persistence,” Review of Financial Studies, Vol. 31, No. 5, May 2018, 1896-1929. 119 Franklin Allen, “Do Financial Institutions Matter?” Journal of Finance, Vol. 56, No. 4, August 2001,

1165-1175. For more recent work that includes financial institutions, see Tobias Adrian, Erkko Etula, and

Tyler Muir, “Financial Intermediaries and the Cross-Section of Asset Returns,” Journal of Finance, Vol. 69,

No. 6, December 2014, 2557-2596 and Valentin Haddad and Tyler Muir, “Do Intermediaries Matter for

Aggregate Asset Prices?” NBER Working Paper, April 25, 2018. 120 Charles D. Ellis, “Will Business Success Spoil the Investment Management Profession?” Journal of

Portfolio Management, Vol. 27, No. 3, Spring 2001, 11-15. 121 Kenneth R. French, “The Cost of Active Investing,” Journal of Finance, Vol. 63, No. 4, August 2008,

1537-1573. 122 Andrei Shleifer, Inefficient Markets: An Introduction to Behavioral Finance (Oxford: Oxford University

Press, 2000), 2-10. 123 Milton Friedman, Essays in Positive Economics (Chicago, IL: University of Chicago Press, 1953), 21. 124 Donald MacKenzie, “Long-Term Capital Management and the Sociology of Arbitrage,” Economy

and Society, Vol. 32, No. 3, August 2003, 349-380. 125 James Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How

Collective Wisdom Shapes Business, Economies, Societies and Nations (New York: Doubleday, 2004);

Robert E. Verrecchia, “Consensus Beliefs, Information Acquisition, and Market Information Efficiency,”

American Economic Review, Vol. 70, No. 5, December 1980, 874-884; Richard P. Mann and Dirk

Helbing, “Optimal Incentives for Collective Intelligence,” PNAS, Vol. 114, No. 20, May 16, 2017, 5077-

5082; Chia-Hsuan Yeh and Shu-Heng Chen, “Market Diversity and Market Efficiency: The Approach

Based on Genetic Programming,” AISB Journal, Vol. 1, No. 1, December 2001, 147-167; Michael J.

Mauboussin, “Revisiting Market Efficiency: The Stock Market as a Complex Adaptive System,” Journal

of Applied Corporate Finance, Vol. 14, No. 4, Winter 2002, 47-55; Andrew W. Lo, Adaptive Markets:

Financial Evolution at the Speed of Thought (Princeton, NJ: Princeton University Press, 2017); and

Bradford Cornell, “What Is the Alternative Hypothesis to Market Efficiency?” Journal of Portfolio

Management, Vol. 44, No. 7, Summer 2018, 3-6. 126 Jan Lorenz, Heiko Rauhut, Frank Schweitzer, and Dirk Helbing, “How Social Influence Can Undermine

the Wisdom of Crowd Effect,” PNAS, Vol. 108, No. 22, May 31, 2011, 9020-9025. 127 Didier Sornette, Why Stock Markets Crash: Critical Events in Complex Financial Systems (Princeton,

NJ: Princeton University Press, 2003), 137. 128 Kewei Hou, Chen Xue, and Lu Zhang, “Replicating Anomalies,” Review of Financial Studies,

forthcoming; Guanhao Feng, Stefano Giglio, Dacheng Xiu, “Taming the Factor Zoo: A Test of New

Factors,” Working Paper, January 2, 2019; John P. A. Ioannidis, T. D. Stanley, and Hristos Doucouliagos,

“The Power of Bias in Economics Research,” Economic Journal, Vol. 127, No. 605, October 2017, F236-

F265; and Guido Baltussen, Laurens Swinkels, and Pim Van Vliet, “Global Factor Premiums,” Working

Paper, January 2019. 129 John H. Cochrane, “Presidential Address: Discount Rates,” Journal of Finance, Vol. 66, No. 4, August

2001, 1047-1108. For useful discussions of the strengths and weaknesses of factors, see Andrew Ang,

Asset Management: A Systematic Approach to Factor Investing (Oxford, UK: Oxford University Press,


2014) and Antti Ilmanen, Expected Returns: An Investor’s Guide to Harvesting Market Rewards

(Hoboken, NJ: John Wiley & Sons, 2011). 130 William F. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk,”

Journal of Finance, Vol. 19, No. 3, September 1964, 425-442. 131 Rolf W. Banz, “The Relationship Between Return and Market Value of Common Stocks,” Journal of

Financial Economics, Vol. 9, No. 1, March 1981, 3-18 and Eugene F. Fama and Kenneth R. French, “The

Cross-Section of Expected Stock Returns,” Journal of Finance, Vol. 47, No. 2, June 1992, 427-465. 132 Mark M. Carhart, “On Persistence in Mutual Fund Performance,” Journal of Finance, Vol. 52, No. 1,

March 1997, 57-82. 133 Robert Novy-Marx, “The Other Side of Value: The Gross Profitability Premium,” Journal of Financial

Economics, Vol. 108, No. 1, April 2013, 1-28. 134 Michael J. Cooper, Huseyin Gulen, and Michael J. Schill, “Asset Growth and the Cross-Section of

Stock Returns,” Journal of Finance, Vol. 63, No. 4, August 2008, 1609-1651; Akiko Watanabe, Yan Xu,

Tong Yao, and Tong Yu, “The Asset Growth Effect: Insights for International Equity Markets,” Journal of

Financial Economics, Vol. 108, No. 2, May 2013, 259-263; and Sheridan Titman, K. C. John Wei, and

Feixue Xie, “Market Development and the Asset Growth Effect: International Evidence,” Journal of

Financial and Quantitative Analysis, Vol. 48, No. 5, October 2013, 1405-1432. 135 Eugene F. Fama and Kenneth R. French, “A Five-Factor Asset Pricing Model,” Journal of Financial

Economics, Vol. 116, No. 1, April 2015, 1-22. 136 Kent Daniel, David Hirshleifer, and Siew Hong Teoh, “Investor Psychology in Capital Markets:

Evidence and Policy Implications,” Journal of Monetary Economics, Vol. 49, No. 1, January 2002, 139-

209 and Kent Daniel and David Hirshleifer, “Overconfident Investors, Predictable Returns, and Excessive

Trading,” Journal of Economic Perspectives, Vol. 29, No. 4, Fall 2015, 61-88. 137 Andrew Ang, “Factor Checklist: Four Requirements for a Robust Factor,” BlackRock, September 5,

2017. 138 Josef Lakonishok, Andrei Shleifer, and Robert Vishny, “Contrarian Investment, Extrapolation, and

Risk,” Journal of Finance, Vol. 49, No. 5, December 1994, 1541-1578; Chi F. Ling and Simon G. M. Koo,

“On Value Premium, Part II: The Explanations,” Journal of Mathematical Finance, Vol. 2, No. 1, February

2012, 66-74; Nicholas Barberis and Ming Huang, “Mental Accounting, Loss Aversion, and Individual

Stock Returns,” Journal of Finance, Vol. 56, No. 4, August 2001, 1247-1292. 139 Amil Dasgupta, Andrea Prat, and Michela Verardo, “The Price Impact of Institutional Herding,”

Review of Financial Studies, Vol. 24, No. 3, March 2011, 892-925; Nicholas Barberis, Andrei Shleifer, and

Robert Vishny, "A Model of Investor Sentiment," Journal of Financial Economics, Vol. 49, No. 3,

September 1998, 307-343; Kent Daniel, David Hirshleifer, and Avanidhar Subrahmanyam, “Investor

Psychology and Security Market Under- and Overreactions,” Journal of Finance, Vol. 53, No. 6,

December 1998, 1839-1885; and Harrison Hong, Terence Lim, and Jeremy C. Stein, “Bad News Travels

Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies,” Journal of Finance, Vol.

55, No. 1, February 2000, 265-295; Dimitri Vayanos and Paul Woolley, “An Institutional Theory of

Momentum and Reversal,” Review of Financial Studies, Vol. 26, No. 5, May, 2013, 1087-1145. 140 Alok Kumar, “Who Gambles in the Stock Market?” Journal of Finance, Vol. 64, No. 4, August 2009,

1889-1933.


Resources

Books

Ang, Andrew, Asset Management: A Systematic Approach to Factor Investing (Oxford, UK: Oxford

University Press, 2014).

Baker, H. Kent, and John R. Nofsinger, eds., Behavioral Finance: Investors, Corporations, and Markets

(Hoboken, NJ: John Wiley & Sons, 2010).

Campbell, John Y., Andrew W. Lo, and Craig MacKinlay, The Econometrics of Financial Markets

(Princeton, NJ: Princeton University Press, 1997).

Constantinides, George, Milton Harris, and Rene M. Stulz, eds., Handbook of the Economics of Finance

(Amsterdam: Elsevier, 2013).

Damodaran, Aswath, Narrative and Numbers: The Value of Stories in Business (New York: Columbia

Business School Publishing, 2017).

Fisher, Philip A., Common Stocks and Uncommon Profits (New York: Harper & Brothers, 1958).

Friedman, Milton, Essays in Positive Economics (Chicago, IL: University of Chicago Press, 1953).

Gennaioli, Nicola, and Andrei Shleifer, A Crisis of Beliefs: Investor Psychology and Financial Fragility

(Princeton, NJ: Princeton University Press, 2018).

Graham, Benjamin, The Intelligent Investor: The Classic Text on Value Investing, Third Edition (New York:

HarperBusiness, 2005).

Greenblatt, Joel, You Can Be a Stock Market Genius (Even if you’re not too smart!): Uncover the Secret

Hiding Places of Stock Market Profits (New York: Fireside, 1997).

Grossman, Sanford J., The Informational Role of Prices (Cambridge, MA: MIT Press, 1989).

Guetzkow, Harold, ed., Groups, Leadership and Men (Pittsburgh, PA: Carnegie Press, 1951).

Hutchinson, Alex, Endure: Mind, Body, and the Curiously Elastic Limits of Human Performance (New

York: HarperCollins, 2018).

Ilmanen, Antti, Expected Returns: An Investor’s Guide to Harvesting Market Rewards (Hoboken, NJ:

John Wiley & Sons, 2011).

Lo, Andrew W., Adaptive Markets: Financial Evolution at the Speed of Thought (Princeton, NJ: Princeton

University Press, 2017).

MacKenzie, Donald, An Engine, Not a Camera: How Financial Models Shape Markets (Cambridge, MA:

MIT Press, 2006).

Mauboussin, Michael J., More Than You Know: Finding Financial Wisdom in Unconventional Places,

Updated and Expanded (New York: Columbia Business School Publishing, 2008).

Page, Scott E., The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and

Societies (Princeton, NJ: Princeton University Press, 2007).

_____., The Model Thinker: What You Need to Know to Make Data Work for You (New York: Basic Books,

2018).

Pedersen, Lasse Heje, Efficiently Inefficient: How Smart Money Invests and Market Prices Are

Determined (Princeton, NJ: Princeton University Press, 2015).

Rappaport, Alfred, and Michael J. Mauboussin, Expectations Investing: Reading Stock Prices for Better

Returns (Boston, MA: Harvard Business School Press, 2001).

Sapolsky, Robert M., Why Zebras Don’t Get Ulcers: An Updated Guide to Stress, Stress-Related Disease,

and Coping (New York: W. H. Freeman and Company, 1994).

Schwager, Jack D., Hedge Fund Market Wizards: How Winning Traders Win (Hoboken, NJ: John Wiley &

Sons, 2012).

Shleifer, Andrei, Inefficient Markets: An Introduction to Behavioral Finance (Oxford: Oxford University

Press, 2000).

Smith, Vernon L., Papers in Experimental Economics (Cambridge, UK: Cambridge University Press, 1991).


Sornette, Didier, Why Stock Markets Crash: Critical Events in Complex Financial Systems (Princeton, NJ:

Princeton University Press, 2003).

Surowiecki, James, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How

Collective Wisdom Shapes Business, Economies, Societies and Nations (New York: Doubleday, 2004).

Swensen, David F., Pioneering Portfolio Management: An Unconventional Approach to Institutional

Management (New York: Free Press, 2000).

Tetlock, Philip E., Expert Political Judgment: How Good Is It? How Can We Know? (Princeton, NJ:

Princeton University Press, 2005).

Zacks, Leonard, ed., The Handbook of Equity Market Anomalies: Translating Market Inefficiencies into

Effective Investment Strategies (Hoboken, NJ: John Wiley & Sons, 2011).


Articles and Papers

Adrian, Tobias, Erkko Etula, and Tyler Muir, “Financial Intermediaries and the Cross-Section of Asset

Returns,” Journal of Finance, Vol. 69, No. 6, December 2014, 2557-2596.

Ahoniemi, Katja, and Petri Jylhä, “Flows, Price Pressure, and Hedge Fund Returns,” Financial Analysts

Journal, Vol. 70, No. 5, September/October 2014, 73-93.

Akbas, Ferhat, Will J. Armstrong, Sorin Sorescu, Avanidhar Subrahmanyam, “Smart Money, Dumb

Money, and Capital Market Anomalies,” Journal of Financial Economics, Vol. 118, No. 2, November

2015, 355-382.

Allen, Franklin, “Do Financial Institutions Matter?” Journal of Finance, Vol. 56, No. 4, August 2001, 1165-

1175.

Andonov, Aleksandar, and Joshua D. Rauh, “The Return Expectations of Institutional Investors,” Stanford

University Graduate School of Business Research Paper No. 18-5, November 19, 2018.

Ang, Andrew, “Factor Checklist: Four Requirements for a Robust Factor,” BlackRock, September 5,

2017.

Ang, Andrew, William N. Goetzmann, and Stephen M. Schaefer, “The Efficient Market Theory and

Evidence: Implications for Active Investment Management,” Foundations and Trends in Accounting,

Vol. 5, No. 3, 2010, 157-242.

Arnold, Tom, John H. Earl, Jr., and David S. North, “Are Cover Stories Effective Contrarian Indicators?”

Financial Analysts Journal, Vol. 63, No. 2, March/April 2007, 70-75.

Arnott, Rob, Vitali Kalesnik, and Lillian Wu, “The Folly of Hiring Winners and Firing Losers,” Journal of

Portfolio Management, Vol. 45, No. 1, Fall 2018, 71-84.

Asness, Clifford S., Tobias J. Moskowitz, and Lasse Heje Pedersen, “Value and Momentum Everywhere,”

Journal of Finance, Vol. 68, No. 3, June 2013, 929-985.

Baker, Malcolm, and Jeffrey Wurgler, “Investor Sentiment and the Cross-Section of Stock Returns,”

Journal of Finance, Vol. 61, No. 4, August 2006, 1645-1680.

_____., “Investor Sentiment in the Stock Market,” Journal of Economic Perspectives, Vol. 21, No. 2,

Spring, 2007, 129-151.

Bali, Turan G., Nusret Cakici, Robert F. Whitelaw, “Maxing Out: Stocks as Lotteries and the Cross-Section

of Expected Returns,” Journal of Financial Economics, Vol. 99, No. 2, February 2011, 427-446.

Baltussen, Guido, Laurens Swinkels, and Pim Van Vliet, “Global Factor Premiums,” Working Paper,

January 2019.

Banz, Rolf W., “The Relationship Between Return and Market Value of Common Stocks,” Journal of

Financial Economics, Vol. 9, No. 1, March 1981, 3-18.

Barber, Brad M., and John D. Lyon, “Detecting Long-run Abnormal Stock Returns: The Empirical Power

and Specification of Test Statistics,” Journal of Financial Economics, Vol. 43, No. 3, March 1997, 341-

372.

Barber, Brad M., and Terrance Odean, “Boys will be Boys: Gender, Overconfidence, and Common

Stock Investment,” Quarterly Journal of Economics, Vol. 116, No. 1, February 2001, 261-292.

_____., “All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and

Institutional Investors,” Review of Financial Studies, Vol. 21, No. 2, March 2008, 785-818.

_____., “The Behavior of Individual Investors,” in George Constantinides, Milton Harris, and Rene M. Stulz,

eds., Handbook of the Economics of Finance (Amsterdam: Elsevier, 2013), 1533-1570.

Barber, Brad M., Yi-Tsung Lee, Yu-Jane Liu, Terrance Odean, and Ke Zhang, “Learning Fast or Slow,”

Working Paper, December 27, 2018.

Barberis, Nicholas C., “Psychology-based Models of Asset Prices and Trading Volume,” NBER Working

Paper No. 24723, June 2018.

Barberis, Nicholas, Andrei Shleifer, and Robert Vishny, "A Model of Investor Sentiment," Journal of

Financial Economics, Vol. 49, No. 3, September 1998, 307-343.


Barberis, Nicholas, Ming Huang, and Tano Santos, Prospect Theory and Asset Prices,” Quarterly Journal

of Economics, Vol. 116, No. 1, February 2001, 1-53.

Barberis, Nicholas, and Richard H. Thaler, “A Survey of Behavioral Finance” in George Constantinides,

Milton Harris, and Rene M. Stulz, eds., Handbook of the Economics of Finance (Amsterdam: Elsevier,

2003), 1053-1128.

Barberis, Nicholas, and Ming Huang, “Mental Accounting, Loss Aversion, and Individual Stock Returns,”


Bayraktar, Mehmet K., Stuart Doole, Altaf Kassam, and Stan Radchenko, “Lost in the Crowd? Identifying

and Measuring Crowded Strategies and Trades, MSCI, June 2015.

Ben-Rephael, Azi, Shmuel Kandel, and Avi Wohl, “Measuring Investor Sentiment with Mutual Fund Flows,

Journal of Financial Economics, Vol. 104, No. 2, May 2012, 363-382.

Benartzi, Shlomo, and Richard H. Thaler, “Myopic Loss Aversion and the Equity Premium Puzzle,”

Quarterly Journal of Economics, Vol. 110, No. 1, February 1995, 73-92.

Beneish, Messod D., “The Detection of Earnings Manipulation,” Financial Analysts Journal, Vol. 55, No. 5,

September/October 1999, 24-36.

Berns, Gregory S., Jonathan Chappelow, Caroline F. Zink, Giuseppe Pagnoni, Megan E. Martin-Skurski,

and Jim Richards, “Neurobiological Correlates of Social Conformity and Independence During Mental

Rotation,” Biological Psychiatry, Vol. 58, No. 3, August 2005, 245-253.

Berk, Jonathan B., and Jules H. van Binsbergen, “Measuring Skill in the Mutual Fund Industry,” Journal of

Financial Economics, Vol. 118, No. 1, October 2015, 1-20.

Bikhchandani, Sushil, David Hirshleifer, and Ivo Welch, “A Theory of Fads, Fashion, Custom, and Cultural

Change as Informational Cascades,” Journal of Political Economy, Vol. 100, No. 5, October 1992, 992-

1026.

Black, Fischer, “Noise,” Journal of Finance, Vol. 41, No. 3, July 1986, 529-543.

Bonne, George, Leon Roisenberg, Roman Kouzmenko, and Peter Zangari, “MSCI Integrated Factor

Crowding Model: Assessing Crowding Risks in Equity Factor Strategies,” MSCI, June 2018.

Boyson, Nicole, Jean Helwege, and Jan Jindra, “Crises, Liquidity Shocks, and Fire Sales at Commercial

Banks,” Financial Management, Vol. 43, No. 4, Winter 2014, 857-884.

Bradshaw, Mark, Yonca Ertimur, and Patricia O’Brien, “Financial Analysts and Their Contribution to Well-

Functioning Capital Markets,” Foundations and Trends in Accounting, Vol. 11, No. 3, 2016, 119-191.

Brunnermeier, Markus K., and Stefan Nagel, “Hedge Funds and the Technology Bubble,” Journal of

Finance, Vol. 59, No. 5, October 2004, 2013-2040.

Buffett, Warren E., “Letter to Shareholders,” Berkshire Hathaway Annual Report, 1987.

Cahan, Rochester, and Yin Luo, “Standing Out From the Crowd: Measuring Crowding in Quantitative

Strategies,” Journal of Portfolio Management, Vol. 39, No. 4, Summer 2013, 14-23.

Cao, Charles, Bing Liang, Andrew W. Lo, and Lubomir Petrasek, “Hedge Fund Holdings and Stock

Market Efficiency,” Review of Asset Pricing Studies, Vol. 8, No. 1, June 2018, 77-116.

Carhart, Mark M., “On Persistence in Mutual Fund Performance,” Journal of Finance, Vol. 52, No. 1,

March 1997, 57-82.

Cen, Ling, Michael G. Hertzel, and Christoph Schiller, “Speed Matters: Limited Attention and Supply-

Chain Information Diffusion,” SSRN Working Paper, January 25, 2018.

Chen, M. Keith, Venkat Lakshminarayanan, and Laurie R. Santos, “How Basic Are Behavioral Biases?

Evidence from Capuchin Monkey Trading Behavior,” Journal of Political Economy, Vol. 114, No. 3, June

2006, 517-537.

Chordia, Tarun, Amit Goyal, Yoshio Nozawa, Avanidhar Subrahmanyam, and Qing Tong, “Are Capital

Market Anomalies Common to Equity and Corporate Bond Markets? An Empirical Investigation,”

Journal of Financial and Quantitative Analysis, Vol. 52, No. 4, August 2017, 1301-1342.

Christoffersen, Susan E.K., David K. Musto, and Russ Wermers, “Investor Flows to Asset Managers: Causes

and Consequences,” Annual Review of Financial Economics, Vol. 6, 2014, 289-310.


Chu, Yongqiang, David A. Hirshleifer, and Liang Ma, “The Causal Effect of Limits to Arbitrage on Asset

Pricing Anomalies,” SSRN Working Paper, July 24, 2018.

Cochrane, John H., “Presidential Address: Discount Rates,” Journal of Finance, Vol. 66, No. 4, August

2001, 1047-1108.

Cohen, Lauren, and Andrea Frazzini,” Economic Links and Predictable Returns,” Journal of Finance,

Vol. 63, No. 4, August 2008, 1977-2011.

Cohen, Lauren, and Dong Lou, “Complicated Firms,” Journal of Financial Economics, Vol. 104, No. 2,

May 2012, 383-400.

Cohen, Lauren, Christopher Malloy, Quoc Nguyen, “Lazy Prices,” NBER Working Paper No. 25084,

September 2018.

Cooper, Michael J., Huseyin Gulen, and Michael J. Schill, “Asset Growth and the Cross-Section of Stock

Returns,” Journal of Finance, Vol. 63, No. 4, August 2008, 1609-1651.

Cornell, Bradford, “What Moves Stock Prices: Another Look,” Journal of Portfolio Management, Vol. 39,

No. 3, Spring 2013, 32-38.

_____., “What Is the Alternative Hypothesis to Market Efficiency?” Journal of Portfolio Management, Vol.

44, No. 7, Summer 2018, 3-6.

Cusatis, Patrick J., James A. Miles, and Randall Woolridge, “Restructuring through Spinoffs: The Stock

Market Evidence,” Journal of Financial Economics, Vol. 33, No. 3, June 1993, 293-311.

Cusick, Philip A., “Price Effects of Addition or Deletion from the Standard & Poor's 500 Index: Evidence

of Increasing Market Efficiency,” Financial Markets, Institutions and Instruments, Vol. 11, No.4,

November 2002, 349-383.

Cutler, David M., James M. Poterba, and Lawrence H. Summers, “What Moves Stock Prices?” Journal of

Portfolio Management, Vol. 15, No. 3, Spring 1989, 4-12.

Daniel, Kent, David Hirshleifer, and Avanidhar Subrahmanyam, “Investor Psychology and Security

Market Under- and Overreactions,” Journal of Finance, Vol. 53, No. 6, December 1998, 1839-1885.

Daniel, Kent, David Hirshleifer, and Siew Hong Teoh, “Investor Psychology in Capital Markets: Evidence

and Policy Implications,” Journal of Monetary Economics, Vol. 49, No. 1, January 2002, 139-209.

Daniel, Kent, and David Hirshleifer, “Overconfident Investors, Predictable Returns, and Excessive

Trading,” Journal of Economic Perspectives, Vol. 29, No. 4, Fall 2015, 61-88.

_____., “Overconfident Investors, Predictable Returns, and Excessive Trading,” Journal of Economic

Perspectives, Vol. 29, No. 4, Fall 2015, 61-88.

Kent Daniel and Tobias J. Moskowitz, “Momentum Crashes,” Journal of Financial Economics, Vol. 122,

No. 2, November 2016, 221-247.

Daniel, Kent, David Hirshleifer, and Lin Sun, “Short- and Long-Horizon Behavioral Factors,” Columbia

Business School Research Paper No. 18-5, December 29, 2018.

Dasgupta, Amil, Andrea Prat, and Michela Verardo, “The Price Impact of Institutional Herding,” Review

of Financial Studies, Vol. 24, No. 3, March 2011, 892-925.

Dave, Chetan, and Katherine W. Wolfe, “On Confirmation Bias and Deviations From Bayesian

Updating,” Working Paper, March 21, 2003.

Desai, Hemang, and Prem C. Jain, “Firm Performance and Focus: Long-Run Stock Market Performance

Following Spinoffs,” Journal of Financial Economics, Vol. 54, No. 1, October 1999, 75-101.

Donoho, David L., Robert A. Crenian, and Matthew H. Scanlan, “Is Patience a Virtue? The

Unsentimental Case for the Long View in Evaluating Returns,” Journal of Portfolio Management, Vol. 37,

No. 1, Fall 2010, 105-120.

Dong, Ming, David Hirshleifer, and Siew Hong Teoh, “Overvalued Equity and Financing Decisions,”

Review of Financial Studies, Vol. 25, No. 12, December 2012, 3645–3683.

Dyck, Alexander, Karl V. Lins, and Lukasz Pomorski, “Does Active Management Pay? New International

Evidence,” Review of Asset Pricing Studies, Vol. 3, No. 2, December 2013, 200-228.


Ederington, Louis H., Jeremy Goh, Yen Teik Lee, and Lisa Yang, “Are Bond Ratings Informative?

Evidence from Regulatory Regime Changes,” Research Collection Lee Kong Chian School of Business,

May 2018, 1-33.

Ellis, Charles D., “The Loser’s Game,” Financial Analysts Journal, Vol. 31, No. 4, July/August 1975, 19-26.

_____., “Will Business Success Spoil the Investment Management Profession?” Journal of Portfolio

Management, Spring 2001, 11-15.

Ellul, Andrew, Chotibhak Jotikasthira, and Christian T. Lundblad, “Regulatory Pressure and Fire Sales in

the Corporate Bond Market,” Journal of Financial Economics, Vol. 101, No. 3, September 2011, 596-620.

Engelberg, Joseph, “Costly Information Processing: Evidence from Earnings Announcements,” AFA 2009

San Francisco Meetings Paper, January 18, 2008.

Engelberg, Joseph, Caroline Sasseville, and Jared Williams, “Market Madness? The Case of ‘Mad

Money,’” Management Science, Vol. 58, No. 2, February 2012, 351-364.

Fama, Eugene F., “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of

Finance, Vol. 25, No. 2, May 1970, 383-417.

_____., “Efficient Capital Markets: II,” Journal of Finance, Vol. 46, No. 5, December 1991, 1575-1617.

_____., “Market Efficiency, Long-term Returns, and Behavioral Finance,” Journal of Financial Economics,

Vol. 49, No. 3, September 1998, 283-306.

Fama, Eugene F., and Kenneth R. French, “The Cross-Section of Expected Stock Returns,” Journal of

Finance, Vol. 47, No. 2, June 1992, 427-465.

_____., “A Five-Factor Asset Pricing Model,” Journal of Financial Economics, Vol. 116, No. 1, April 2015, 1-

22.

Feng, Guanhao, Stefano Giglio, Dacheng Xiu, “Taming the Factor Zoo: A Test of New Factors,” Working

Paper, January 2, 2019.

Feldman, Todd, and Shuming Liu, “Contagious Investor Sentiment and International Markets,” Journal

of Portfolio Management, Vol. 43, No. 4, Summer 2017, 125-136.

Frazzini, Andrea, Owen A. Lamont, “Dumb Money: Mutual Fund Flows and the Cross-section of Stock

Returns,” Journal of Financial Economics, Vol. 88, No. 2, May 2008, 299-322.

Frazzini, Andrea, Ronen Israel, and Tobias Moskowitz, “Trading Costs,” SSRN Working Paper, April 7, 2018.

French, Kenneth R., “The Cost of Active Investing,” Journal of Finance, Vol. 63, No. 4, August 2008, 1537-

1573.

Friesen, Geoffrey C., and Travis R. A. Sapp, “Mutual Fund Flows and Investor Returns: An Empirical

Examination of Fund Investor Timing Ability,” Journal of Banking & Finance, Vol. 31, No. 9, September

2007, 2796-2816.

Gargano, Antonio, Alberto G. Rossi, and Russ Wermers, “The Freedom of Information Act and the Race

Toward Information Acquisition,” Review of Financial Studies, Vol. 30, No. 6, June 2017, 2179-2228.

Gârleanu, Nicolae, and Lasse Heje Pedersen, “Efficiently Inefficient Markets for Assets and Asset

Management,” Journal of Finance, Vol. 73, No. 4, August 2018, 1663-1712.

Geanakoplos, John, “The Leverage Cycle,” Cowles Foundation Discussion Paper No.1715R, January

2010.

Gilson, Ronald J., and Reinier H. Kraakman, “The Mechanisms of Market Efficiency,” Virginia Law

Review, Vol. 70, No. 4, May, 1984, 549-644.

Gode, Dhananjay, and Shyam Sunder, “Allocative Efficiency of Markets with Zero-Intelligence Traders,”

Journal of Political Economy, Vol. 101, No. 1, February 1993, 119-137.

Gompers, Paul A., and Andrew Metrick, “Institutional Investors and Equity Prices,” Quarterly Journal of

Economics, Vol. 116, No. 1, February 2001, 229-259.

Goyal, Amit, and Sunil Wahal, “The Selection and Termination of Investment Management Firms by

Plan Sponsors,” Journal of Finance, Vol 63, No. 4, August 2008, 1805-1847.

Goyal, Amit, Antti Ilmanen, and David Kabiller, “Bad Habits and Good Practices,” Journal of Portfolio

Management, Vol. 41, No. 4, Summer 2015, 97-107.


Granovetter, Mark, “Threshold Models of Collective Behavior,” American Journal of Sociology, Vol. 83,

No. 6, May, 1978, 1420-1443.

Greenwood, Robin, and Andrei Shleifer, “Expectations of Returns and Expected Returns,” Review of

Financial Studies, Vol. 27, No. 3, March 2014, 714-746.

Greenwood, Robin, Andrei Shleifer, and Yang You, “Bubbles for Fama,” Journal of Financial Economics,

Vol. 131, No. 1, January 2019, 20-43.

Griffin, Dale, and Amos Tversky, “The Weighing of Evidence and the Determinants of Confidence,”

Cognitive Psychology, Vol. 24, No. 3, July 1992, 411-435.

Grinblatt, Mark, and Matti Keloharju, “Sensation Seeking, Overconfidence, and Trading Activity,”

Journal of Finance, Vol. 64, No. 2, April 2009, 549-578.

Grossman, Sanford J., and Joseph E. Stiglitz, “On the Impossibility of Informationally Efficient Markets,”

American Economic Review, Vol. 70, No. 3, June 1980, 393-408.

Gurley, Bill, “How to Miss By a Mile: An Alternative Look at Uber’s Potential Market Size,” Above the

Crowd, July 11, 2014.

Haddad, Valentin, and Tyler Muir, “Do Intermediaries Matter for Aggregate Asset Prices?” NBER Working

Paper, April 25, 2018.

Haghani, Victor, and Samantha McBride, “Return Chasing and Trend Following: Superficial Similarities

Mask Fundamental Differences,” SSRN Working Paper, January 29, 2016.

Haigh, Michael S., and John A. List, “Do Professional Traders Exhibit Myopic Loss Aversion? An

Experimental Analysis,” Journal of Finance, Vol. 60, No. 1, February 2005, 523-534.

Han, Bing, David A. Hirshleifer, and Johan Walden, “Social Transmission Bias and Investor Behavior,”

Rotman School of Management Working Paper No. 3053655, June 19, 2018.

Hartzmark, Samuel M., and Kelly Shue, “A Tough Act to Follow: Contrast Effects in Financial Markets,”


Harvey, Campbell R., Yan Liu, and Heqing Zhu, “… and the Cross-Section of Expected Returns,” Review

of Financial Studies, Vol. 29, No. 1, January 2016, 5-68.

Hirshleifer, David, “Behavioral Finance,” Annual Review of Financial Economics, Vol. 7, 2015, 133-159.

Hirshleifer, David, and Siew Hong Teoh, “Limited Attention, Information Disclosure, and Financial

Reporting,” Journal of Accounting and Economics, Vol. 36, No. 1-3, December 2003, 337-386.

Hoberg, Gerard, Nitin Kumar, and Nagpurnanand Prabhala, “Mutual Fund Competition, Managerial

Skill, and Alpha Persistence,” Review of Financial Studies, Vol. 31, No. 5, May 2018, 1896-1929.

Hong, Harrison, and Jeremy C. Stein, “A Unified Theory of Underreaction, Momentum Trading, and

Overreaction in Asset Markets,” Journal of Finance, Vol. 54, No. 6, December 1999, 2143-2184.

Hong, Harrison, Terence Lim, and Jeremy C. Stein, “Bad News Travels Slowly: Size, Analyst Coverage,

and the Profitability of Momentum Strategies,” Journal of Finance, Vol. 55, No. 1, February 2000, 265-

295.

_____., “Disagreement and the Stock Market,” Journal of Economic Perspectives, Vol. 21, No. 2, Spring

2007, 109-128.

Hou, Kewei ,Chen Xue, and Lu Zhang, “Replicating Anomalies,” Review of Financial Studies,

forthcoming.

Hribar, Paul, and John McInnis, “Investor Sentiment and Analysts’ Earnings Forecast Errors,”

Management Science, Vol. 58, No. 2, February 2012, 293-307.

Huang, Dashan, Fuwei Jiang, Jun Tu, and Guofu Zhou, “Investor Sentiment Aligned: A Powerful

Predictor of Stock Returns,” Review of Financial Studies, Vol. 28, No. 3, March 2015, 791-837.

Huberman, Gur, and Tomer Regev, “Contagious Speculation and a Cure for Cancer: A Nonevent that

Made Stock Prices Soar,” Journal of Finance, Vol. 56, No. 1, February 2001, 387-396.

Ilmanen, Antti, “Who Is on the Other Side?” Presentation at the Q Group, Fall 2016.

Imas, Alex, “The Realization Effect: Risk-Taking after Realized versus Paper Losses,” American Economic

Review, Vol. 106, No. 8, August 2016, 2086-2109.


Ioannidis, John P. A., T. D. Stanley, and Hristos Doucouliagos, “The Power of Bias in Economics

Research,” Economic Journal, Vol. 127, No. 605, October 2017, F236-F265.

Israel, Ronen, Tobias Moskowitz, Adrienne Ross, Laura Serban, “Implementing Momentum: What Have

We Learned?” SSRN Working Paper, December 2017.

Jacobs, Heiko and Sebastian Müller, “Anomalies Across the Globe: Once Public, No Longer Existent,”

Journal of Financial Economics, Forthcoming, October 2018.

Jensen, Michael C., “Some Anomalous Evidence Regarding Market Efficiency,” Journal of Financial

Economics, Vol. 6, Nos. 2-3, June-September 1978, 95-101.

Jiang, George, Ke Shen, Russ Wermers, and Tong Yao, “Costly Information Production, Information

Intensity, and Mutual Fund Performance,” SSRN Working Paper, November 21, 2018.

Kahneman, Daniel, and Amos Tversky, “Prospect Theory: An Analysis of Decision under Risk,”

Econometrica, Vol. 47, No. 2, March 1979, 263-292.

Kolata, Gina, “Hope in the Lab: A Cautious Awe Greets Drugs That Eradicate Tumors in Mice,” New

York Times, May 3, 1998.

Kumar, Alok, “Who Gambles in the Stock Market?” Journal of Finance, Vol. 64, No. 4, August 2009,

1889-1933.

Kunte, Shreenivas, “The Herding Mentality: Behavioral Finance and Investor Biases,” Enterprising

Investor, August 6, 2015.

Lakonishok, Josef, Andrei Shleifer, and Robert Vishny, “Contrarian Investment, Extrapolation, and Risk,”

Journal of Finance, Vol. 49, No. 5, December 1994, 1541-1578.

Lamont, Owen A., and Richard H. Thaler, “Anomalies: The Law of One Price in Financial Markets,”

Journal of Economic Perspectives, Vol. 17, No. 4, Fall 2003, 191-202.

Lan, Chunhua, Fabio Moneta, and Russell R. Wermers, “Holding Horizon: A New Measure of Active

Investment Management,” American Finance Association Meetings 2015 Paper, July 1, 2018.

Larson, Francis, John A. List, Robert D. Metcalfe, “Can Myopic Loss Aversion Explain the Equity Premium

Puzzle? Evidence from a Natural Field Experiment with Professional Traders,” NBER Working Paper No.

22605, September 2016.

LeBaron, Blake, “Financial Market Efficiency in a Coevolutionary Environment,” Proceedings of the

Workshop on Simulation of Social Agents: Architectures and Institutions, Argonne National Laboratory

and University of Chicago, October 2000, Argonne 2001, 33-51.

Lee, Boram, and Yulia Veld-Merkoulova, “Myopic Loss Aversion and Stock Investments: An Empirical

Study of Private Investors,” Journal of Banking & Finance, Vol. 70, September 2016, 235-246.

Lee, Charles M.C., and Eric So, “Alphanomics: The Informational Underpinnings of Market Efficiency,”

Foundations and Trends in Accounting, Vol. 9, No. 2-3, 2014, 59-258.

Li, Jun, and Jianfeng Yu, “Investor Attention, Psychological Anchors, and Stock Return Predictability,”

Journal of Financial Economics, Vol. 104, No. 2, May 2012, 401-419.

Lim, Sonya S., and Siew Hong Teoh, “Limited Attention,” in H. Kent Baker and John R. Nofsinger, eds.,

Behavioral Finance: Investors, Corporations, and Markets (Hoboken, NJ: John Wiley & Sons, 2010), 295-

312.

Ling, Chi F., and Simon G. M. Koo, “On Value Premium, Part II: The Explanations,” Journal of

Mathematical Finance, Vol. 2, No. 1, February 2012, 66-74.

Linnainmaa, Juhani T., “Reverse Survivorship Bias,” Journal of Finance, Vol. 68, No. 3, June 2013, 789-

813.

Lorenz, Jan, Heiko Rauhut, Frank Schweitzer, and Dirk Helbing, “How Social Influence Can Undermine

the Wisdom of Crowd Effect,” PNAS, Vol. 108, No. 22, May 31, 2011, 9020-9025.

Lou, Dong, “A Flow-Based Explanation for Return Predictability,” Review of Financial Studies, Vol. 25,

No. 12, December 2012, 3457-3489.

MacKenzie, Donald, “Long-Term Capital Management and the Sociology of Arbitrage,” Economy and

Society, Vol. 32, No. 3, August 2003, 349-380.


Malkiel, Burton G., “Reflections on the Efficient Market Hypothesis: 30 Years Later,” Financial Review, Vol.

40, No. 1, February 2005, 1-9.

Mann, Richard P., and Dirk Helbing, “Optimal Incentives for Collective Intelligence,” PNAS, Vol. 114, No.

20, May 16, 2017, 5077-5082.

Massey, Cade and George Wu, “Detecting Regime Shifts: The Causes of Under- and Overreaction,”

Management Science, Vol. 51, No. 6, June 2005, 932–947.

Mauboussin, Michael J., “Revisiting Market Efficiency: The Stock Market as a Complex Adaptive

System,” Journal of Applied Corporate Finance, Vol. 14, No. 4, Winter 2002, 47-55.

_____., “The True Measures of Success,” Harvard Business Review, October 2012, 46-56.

Mazur, Mieszko, “Creating M&A Opportunities through Corporate Spin-Offs,” Journal of Applied

Corporate Finance, Vol. 27, No. 3, Summer 2015, 137-143.

Mbanga, Cedric, Ali F. Darrat, Jung Chul Park, “Investor Sentiment and Aggregate Stock Returns: The

Role of Investor Attention,” Review of Quantitative Finance and Accounting, 2018.

McConnell, John J., and Alexei V. Ovtchinnikov, “Predictability of Long-Term Spinoff Returns,” Journal of

Investment Management, Vol. 2, No. 3, Third Quarter 2004, 35-44.

McConnell, John J., Steven E. Sibley, and Wei Xu, “The Stock Price Performance of Spin-Off Subsidiaries,

Their Parents, and the Spin-Off ETF, 2001–2013,” Journal of Portfolio Management, Fall 2015, 143-152.

McLean, R. David, and Jeffrey Pontiff, “Does Academic Research Destroy Stock Return Predictability?”

Journal of Finance, Vol. 71, No. 1, February 2016, 5-32.

Miao, Bin, Siew Hong Teoh, and Zinan Zhu, “Limited Attention, Statement of Cash Flow Disclosure, and

the Valuation of Accruals,” Review of Accounting Studies, Vol. 21, No. 2, June 2016, 473-515.

Moore, Don A., and Derek Schatz, “The Three Faces of Overconfidence,” Social and Personality

Psychology Compass, Vol. 11, No. 8, August 2017.

Novy-Marx, Robert, “The Other Side of Value: The Gross Profitability Premium,” Journal of Financial

Economics, Vol. 108, No. 1, April 2013, 1-28.

Patel, Nimesh, and Ivo Welch, “Extended Stock Returns in Response to S&P 500 Index Changes,”

Review of Asset Pricing Studies, Vol. 7, No. 2, December 2017, 172-208.

Pedersen, Lasse Heje, “When Everyone Runs for the Exit,” International Journal of Central Banking, Vol.

5, No. 4, December 2009, 177-199.

_____., “Sharpening the Arithmetic of Active Management,” Financial Analysts Journal, Vol. 74, No. 1,

First Quarter 2018, 21-36.

Petajisto, Antti, “Why Do Demand Curves for Stocks Slope Down?” Journal of Financial and

Quantitative Analysis, Vol. 44, No. 5, October 2009, 1013-1044.

_____., “Inefficiencies in the Pricing of Exchange-Traded Funds,” Financial Analysts Journal, Spring 2017,

24-54.

Pástor, Ĺuboš, and Robert F Stambaugh, “Liquidity Risk and Expected Stock Returns,” Journal of Political

Economy, Vol. 111, No. 3, June 2003, 642-685.

Patton, Andrew J., and Brian M. Weller, “What You See Is Not What You Get: The Costs of Trading

Market Anomalies,” Economic Research Initiatives at Duke (ERID) Working Paper No. 255, November

28, 2018.

Rohleder, Martin, Dominik Schulte, Janik Syryca, and Marco Wilkens, “Mutual Fund Stock‐Picking Skill:

New Evidence from Valuation‐ versus Liquidity‐Motivated Trading,” Financial Management, Vol. 47, No.

2, Summer 2018, 309-347.

Russo, J. Edward, and Paul J.H. Schoemaker, "Managing Overconfidence," Sloan Management

Review, Vol. 33, No. 2, Winter 1992, 7-17.

Salganik, Matthew J., Peter Sheridan Dodds, and Duncan J. Watts, “Experimental Study of Inequality

and Unpredictability in an Artificial Cultural Market,” Science, Vol. 311, No. 5762, February 10, 2006, 854-

856.

Scherbina, Anna D., “Asset Price Bubbles: A Survey,” Quantitative Finance, Vol. 14, No. 4, 2014, 589-604.


Scherbina, Anna D., and Bernd Schlusche, “Follow the Leader: Using the Stock Market to Uncover

Information Flows Between Firms,” Review of Finance, forthcoming.

Schnuerch, Robert, and Henning Gibbons, “A Review of Neurocognitive Mechanisms of Social

Conformity,” Social Psychology, Vol. 45, No. 6, November 2014, 466-478.

Schoemaker, Paul J.H., and George S. Day, “How to Make Sense of Weak Signals,” MIT Sloan

Management Review, Vol. 50, No. 3, Spring 2009, 80-89.

Seasholes, Mark S., and Guojun Wu, “Predictable Behavior, Profits, and Attention,” Journal of Empirical

Finance, Vol. 14, No. 5, December 2007, 590-610.

Sharpe, William F., “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk,”

Journal of Finance, Vol. 19, No. 3, September 1964, 425-442.

_____., “The Arithmetic of Active Management,” Financial Analysts Journal, Vol. 47, No. 1,

January/February 1991, 7-9.

Shiller, Robert J., “Stock Prices and Social Dynamics,” Brookings Papers on Economic Activity, Vol. 2,

1984, 457-510.

Shiv, Baba, George Loewenstein, Antoine Bechara, Hanna Damasio, and Antonio R. Damasio,

“Investment Behavior and the Negative Side of Emotion,” Psychological Science, Vol. 16, No. 6, June

2005, 435-439.

Shleifer, Andrei, and Robert W. Vishny, “The Limits of Arbitrage,” Journal of Finance, Vol. 52, No. 1,

March 1997, 35-55.

_____., “Fire Sales in Finance and Macroeconomics,” Journal of Economic Perspectives, Vol. 25, No. 1,

Winter 2011, 29-48.

Sloan, Richard G., and Haifeng You, “Wealth Transfers via Equity Transactions,” Journal of Financial

Economics, Vol. 118, No. 1, October 2015, 93-112.

Soros, George, “General Theory of Reflexivity,” Financial Times, October 26, 2009.

Stein, Jeremy C., “Presidential Address: Sophisticated Investors and Market Efficiency,” Journal of

Finance, Vol. 64, No. 4, August 2009, 1517-1548.

Stewart, Scott D., John J. Neumann, Christopher R. Knittel, and Jeffrey Heisler, “Absence of Value: An

Analysis of Investment Allocation Decisions by Institutional Plan Sponsors,” Financial Analysts Journal,

Vol 65, No. 6, November/December 2009, 34-51.

Thaler, Richard H., “Behavioral Economics: Past, Present, and Future,” American Economic Review, Vol.

106, No. 7, July 2016, 1577-1600.

Titman, Sheridan, K. C. John Wei, and Feixue Xie, “Market Development and the Asset Growth Effect:

International Evidence,” Journal of Financial and Quantitative Analysis, Vol. 48, No. 5, October 2013,

1405-1432.

Treynor, Jack L., “Long-Term Investing,” Financial Analysts Journal, Vol. 32, No. 3, May/June 1976, 56-59.

Tversky, Amos, and Daniel Kahneman, “Belief in the Law of Small Numbers,” Psychological Bulletin, Vol.

76, No. 2, August 1971, 105-110.

Van Binsbergen, Jules H., and Christian Opp, “Real Anomalies,” Journal of Finance, forthcoming.

Vayanos, Dimitri, and Paul Woolley, “An Institutional Theory of Momentum and Reversal,” Review of

Financial Studies, Vol. 26, No. 5, May, 2013, 1087-1145.

Veld, Chris, and Yulia V. Veld-Merkoulova, “Value Creation through Spinoffs: A Review of the Empirical

Evidence,” International Journal of Management Reviews, Vol.11, No. 4, December 2009, 407-420.

Verrecchia, Robert E., “Consensus Beliefs, Information Acquisition, and Market Information Efficiency,”

American Economic Review, Vol. 70, No. 5, December 1980, 874-884.

Warnock, Francis E., and Veronica Cacdac Warnock, “International Capital Flows and U.S. Interest

Rates,” Federal Reserve Discussion Paper, Number 840, September 2005.

Watanabe, Akiko, Yan Xu, Tong Yao, and Tong Yu, “The Asset Growth Effect: Insights for International

Equity Markets,” Journal of Financial Economics, Vol. 108, No. 2, May 2013, 259-263.


Watts, Duncan J., “A Simple Model of Global Cascades on Random Networks, PNAS, Vol. 99, No. 9,

April 30, 2002, 5766-5771.

Welch, Ivo, “Herding among Security Analysts,” Journal of Financial Economics, Vol. 58, No. 3,

December 2000, 369-396.

Wurgler, Jeffrey, “Financial Markets and the Allocation of Capital,” Journal of Financial Economics, Vol.

58, Nos. 1-2, December 2000, 187-214.

Wurgler, Jeffrey, and Ekaterina Zhuravskaya, “Does Arbitrage Flatten Demand Curves for Stocks?”

Journal of Business, Vol. 75, No. 4, October 2002, 583-608.

Yang, David C., and Fan Zhang, “Be Fearful When Households Are Greedy: The Household Equity Share

and Expected Market Returns,” SSRN Working Paper, August 2017.

Yeh, Chia-Hsuan, and Shu-Heng Chen, “Market Diversity and Market Efficiency: The Approach Based

on Genetic Programming,” AISB Journal, Vol. 1, No. 1, December 2001, 147-167.

Zenner, Marc, Evan Junek, and Ram Chivukula, “Shrinking to Grow: Evolving Trends in Corporate Spin-

offs,” Journal of Applied Corporate Finance, Vol. 27, No. 3, Summer 2015, 131-136.

Zhong, Ligang, Xiaoya (Sara) Ding and Nicholas S.P. Tay, “The Impact on Stock Returns of Crowding by

Mutual Funds,” Journal of Portfolio Management, Vol. 43, No. 4, Summer 2017, 87-99.

Zuninoa, Luciano, Massimiliano Zanin, Benjamin M. Tabak, Darío G. Pérez, and Osvaldo A. Rosso,

“Complexity-Entropy Causality Plane: A Useful Approach to Quantify the Stock Market Inefficiency,”

Physica A, Vol. 389, No. 9, May 1, 2010, 1891-1901.


Disclaimers:

This report is provided for informational purposes only and is intended solely for the person to whom it is delivered by BlueMountain

Capital Management, LLC (“BlueMountain”). This report is confidential and may not be reproduced in its entirety or in part, or

redistributed to any party in any form, without the prior written consent of BlueMountain. This report was prepared in good faith by

BlueMountain for your specific use and contains a general market update and information concerning market efficiency.

This report does not constitute an offer to sell or the solicitation of an offer to purchase any securities of any funds or accounts

managed by BlueMountain (the “Funds”). Any such offer or solicitation may be made only by means of the delivery of a confidential

offering memorandum, which will contain material information not included herein and shall supersede, amend and supplement this

report in its entirety. Information contained in this report is accurate only as of its date, regardless of the time of delivery or of any

investment, and does not purport to be complete, nor does BlueMountain undertake any duty to update the information set forth

herein.

This report should not be used as the sole basis for making a decision as to whether or not to invest in the Funds or any other fund or

account managed by BlueMountain. In making an investment decision, you must rely on your own examination of the Funds and

the terms of the offering. You should not construe the contents of these materials as legal, tax, investment or other advice, or a

recommendation to purchase or sell any particular security.

The returns of several market indices are provided in this report as representative of general market conditions and that does not

mean that there necessarily will be a correlation between the returns of any of the Funds, on the one hand, and any of these

indices, on the other hand.

The information included in this report is based upon information reasonably available to BlueMountain as of the date noted herein.

Furthermore, the information included in this report has been obtained from sources that BlueMountain believes to be reliable;

however, these sources cannot be guaranteed as to their accuracy or completeness. No representation, warranty or undertaking,

express or implied, is given as to the accuracy or completeness of the information contained herein, by BlueMountain, its members,

partners or employees, and no liability is accepted by such persons for the accuracy or completeness of any such information.

This report contains certain “forward-looking statements,” which may be identified by the use of such words as “believe,” “expect,”

“anticipate,” “should,” “planned,” “estimated,” “potential,” “outlook,” “forecast,” “plan” and other similar terms. Examples of

forward-looking statements include, without limitation, estimates with respect to financial condition, results of operations, and

success or lack of success of BlueMountain’s investment strategy or the markets generally. All are subject to various factors,

including, without limitation, general and local economic conditions, changing levels of competition within certain industries and

markets, changes in interest rates, changes in legislation or regulation, and other economic, competitive, governmental, regulatory

and technological factors affecting BlueMountain’s operations, each Fund’s operations, and the operations of any portfolio

companies of a Fund, any or all of which could cause actual results to differ materially from projected results.

Documents

BLUEMOUNTAIN INVESTMENT RESEARCH WHO IS ON THE … · BLUEMOUNTAIN INVESTMENT RESEARCH 2Executive Summary If you buy or sell a security and expect an excess return, you should have